diff --git a/.gitignore b/.gitignore index b6e4761..25ff4b0 100644 --- a/.gitignore +++ b/.gitignore @@ -1,129 +1,129 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c282e9a..c97e7ed 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,14 +1,14 @@ -# Contributing - -This project welcomes contributions and suggestions. Most contributions require you to -agree to a Contributor License Agreement (CLA) declaring that you have the right to, -and actually do, grant us the rights to use your contribution. For details, visit -https://cla.microsoft.com. - -When you submit a pull request, a CLA-bot will automatically determine whether you need -to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the -instructions provided by the bot. You will only need to do this once across all repositories using our CLA. - -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). -For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) +# Contributing + +This project welcomes contributions and suggestions. Most contributions require you to +agree to a Contributor License Agreement (CLA) declaring that you have the right to, +and actually do, grant us the rights to use your contribution. For details, visit +https://cla.microsoft.com. + +When you submit a pull request, a CLA-bot will automatically determine whether you need +to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the +instructions provided by the bot. You will only need to do this once across all repositories using our CLA. + +This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). +For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. \ No newline at end of file diff --git a/dopamine/__init__.py b/dopamine/__init__.py index 77bb2a5..1e4b6ec 100644 --- a/dopamine/__init__.py +++ b/dopamine/__init__.py @@ -1,2 +1,2 @@ -# coding=utf-8 -name = 'dopamine' +# coding=utf-8 +name = 'dopamine' diff --git a/dopamine/agents/__init__.py b/dopamine/agents/__init__.py index 920cbb5..dc108ba 100644 --- a/dopamine/agents/__init__.py +++ b/dopamine/agents/__init__.py @@ -1,15 +1,15 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/dopamine/agents/dqn/__init__.py b/dopamine/agents/dqn/__init__.py index 920cbb5..dc108ba 100644 --- a/dopamine/agents/dqn/__init__.py +++ b/dopamine/agents/dqn/__init__.py @@ -1,15 +1,15 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/dopamine/agents/dqn/configs/dqn.gin b/dopamine/agents/dqn/configs/dqn.gin index 13b5727..45ce938 100644 --- a/dopamine/agents/dqn/configs/dqn.gin +++ b/dopamine/agents/dqn/configs/dqn.gin @@ -1,37 +1,37 @@ -# Hyperparameters follow the classic Nature DQN, but we modify as necessary to -# match those used in Rainbow (Hessel et al., 2018), to ensure apples-to-apples -# comparison. -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer -import gin.tf.external_configurables - -DQNAgent.gamma = 0.99 -DQNAgent.update_horizon = 1 -DQNAgent.min_replay_history = 20000 # agent steps -DQNAgent.update_period = 4 -DQNAgent.target_update_period = 8000 # agent steps -DQNAgent.epsilon_train = 0.01 -DQNAgent.epsilon_eval = 0.001 -DQNAgent.epsilon_decay_period = 250000 # agent steps -DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -DQNAgent.optimizer = @tf.train.RMSPropOptimizer() - -tf.train.RMSPropOptimizer.learning_rate = 0.00025 -tf.train.RMSPropOptimizer.decay = 0.95 -tf.train.RMSPropOptimizer.momentum = 0.0 -tf.train.RMSPropOptimizer.epsilon = 0.00001 -tf.train.RMSPropOptimizer.centered = True - -atari_lib.create_atari_environment.game_name = 'Pong' -# Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). -atari_lib.create_atari_environment.sticky_actions = True -create_agent.agent_name = 'dqn' -Runner.num_iterations = 200 -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps - -WrappedReplayBuffer.replay_capacity = 1000000 -WrappedReplayBuffer.batch_size = 32 +# Hyperparameters follow the classic Nature DQN, but we modify as necessary to +# match those used in Rainbow (Hessel et al., 2018), to ensure apples-to-apples +# comparison. +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.agents.dqn.dqn_agent +import dopamine.replay_memory.circular_replay_buffer +import gin.tf.external_configurables + +DQNAgent.gamma = 0.99 +DQNAgent.update_horizon = 1 +DQNAgent.min_replay_history = 20000 # agent steps +DQNAgent.update_period = 4 +DQNAgent.target_update_period = 8000 # agent steps +DQNAgent.epsilon_train = 0.01 +DQNAgent.epsilon_eval = 0.001 +DQNAgent.epsilon_decay_period = 250000 # agent steps +DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +DQNAgent.optimizer = @tf.train.RMSPropOptimizer() + +tf.train.RMSPropOptimizer.learning_rate = 0.00025 +tf.train.RMSPropOptimizer.decay = 0.95 +tf.train.RMSPropOptimizer.momentum = 0.0 +tf.train.RMSPropOptimizer.epsilon = 0.00001 +tf.train.RMSPropOptimizer.centered = True + +atari_lib.create_atari_environment.game_name = 'Pong' +# Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). +atari_lib.create_atari_environment.sticky_actions = True +create_agent.agent_name = 'dqn' +Runner.num_iterations = 200 +Runner.training_steps = 250000 # agent steps +Runner.evaluation_steps = 125000 # agent steps +Runner.max_steps_per_episode = 27000 # agent steps + +WrappedReplayBuffer.replay_capacity = 1000000 +WrappedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/dqn/configs/dqn_acrobot.gin b/dopamine/agents/dqn/configs/dqn_acrobot.gin index 4cf0616..a81f8db 100644 --- a/dopamine/agents/dqn/configs/dqn_acrobot.gin +++ b/dopamine/agents/dqn/configs/dqn_acrobot.gin @@ -1,35 +1,35 @@ -# Hyperparameters for a simple DQN-style Acrobot agent. The hyperparameters -# chosen achieve reasonable performance. -import dopamine.discrete_domains.gym_lib -import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer -import gin.tf.external_configurables - -DQNAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE -DQNAgent.observation_dtype = %gym_lib.ACROBOT_OBSERVATION_DTYPE -DQNAgent.stack_size = %gym_lib.ACROBOT_STACK_SIZE -DQNAgent.network = @gym_lib.acrobot_dqn_network -DQNAgent.gamma = 0.99 -DQNAgent.update_horizon = 1 -DQNAgent.min_replay_history = 500 -DQNAgent.update_period = 4 -DQNAgent.target_update_period = 100 -DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon -DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -DQNAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.001 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -create_gym_environment.environment_name = 'Acrobot' -create_gym_environment.version = 'v1' -create_agent.agent_name = 'dqn' -Runner.create_environment_fn = @gym_lib.create_gym_environment -Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 500 - -WrappedReplayBuffer.replay_capacity = 50000 -WrappedReplayBuffer.batch_size = 128 +# Hyperparameters for a simple DQN-style Acrobot agent. The hyperparameters +# chosen achieve reasonable performance. +import dopamine.discrete_domains.gym_lib +import dopamine.discrete_domains.run_experiment +import dopamine.agents.dqn.dqn_agent +import dopamine.replay_memory.circular_replay_buffer +import gin.tf.external_configurables + +DQNAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE +DQNAgent.observation_dtype = %gym_lib.ACROBOT_OBSERVATION_DTYPE +DQNAgent.stack_size = %gym_lib.ACROBOT_STACK_SIZE +DQNAgent.network = @gym_lib.acrobot_dqn_network +DQNAgent.gamma = 0.99 +DQNAgent.update_horizon = 1 +DQNAgent.min_replay_history = 500 +DQNAgent.update_period = 4 +DQNAgent.target_update_period = 100 +DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon +DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +DQNAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.001 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +create_gym_environment.environment_name = 'Acrobot' +create_gym_environment.version = 'v1' +create_agent.agent_name = 'dqn' +Runner.create_environment_fn = @gym_lib.create_gym_environment +Runner.num_iterations = 500 +Runner.training_steps = 1000 +Runner.evaluation_steps = 1000 +Runner.max_steps_per_episode = 500 + +WrappedReplayBuffer.replay_capacity = 50000 +WrappedReplayBuffer.batch_size = 128 diff --git a/dopamine/agents/dqn/configs/dqn_cartpole.gin b/dopamine/agents/dqn/configs/dqn_cartpole.gin index 9a33508..2f6feff 100644 --- a/dopamine/agents/dqn/configs/dqn_cartpole.gin +++ b/dopamine/agents/dqn/configs/dqn_cartpole.gin @@ -1,35 +1,35 @@ -# Hyperparameters for a simple DQN-style Cartpole agent. The hyperparameters -# chosen achieve reasonable performance. -import dopamine.discrete_domains.gym_lib -import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer -import gin.tf.external_configurables - -DQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE -DQNAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE -DQNAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE -DQNAgent.network = @gym_lib.cartpole_dqn_network -DQNAgent.gamma = 0.99 -DQNAgent.update_horizon = 1 -DQNAgent.min_replay_history = 500 -DQNAgent.update_period = 4 -DQNAgent.target_update_period = 100 -DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon -DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -DQNAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.001 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -create_gym_environment.environment_name = 'CartPole' -create_gym_environment.version = 'v0' -create_agent.agent_name = 'dqn' -Runner.create_environment_fn = @gym_lib.create_gym_environment -Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 200 # Default max episode length. - -WrappedReplayBuffer.replay_capacity = 50000 -WrappedReplayBuffer.batch_size = 128 +# Hyperparameters for a simple DQN-style Cartpole agent. The hyperparameters +# chosen achieve reasonable performance. +import dopamine.discrete_domains.gym_lib +import dopamine.discrete_domains.run_experiment +import dopamine.agents.dqn.dqn_agent +import dopamine.replay_memory.circular_replay_buffer +import gin.tf.external_configurables + +DQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE +DQNAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE +DQNAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE +DQNAgent.network = @gym_lib.cartpole_dqn_network +DQNAgent.gamma = 0.99 +DQNAgent.update_horizon = 1 +DQNAgent.min_replay_history = 500 +DQNAgent.update_period = 4 +DQNAgent.target_update_period = 100 +DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon +DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +DQNAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.001 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +create_gym_environment.environment_name = 'CartPole' +create_gym_environment.version = 'v0' +create_agent.agent_name = 'dqn' +Runner.create_environment_fn = @gym_lib.create_gym_environment +Runner.num_iterations = 500 +Runner.training_steps = 1000 +Runner.evaluation_steps = 1000 +Runner.max_steps_per_episode = 200 # Default max episode length. + +WrappedReplayBuffer.replay_capacity = 50000 +WrappedReplayBuffer.batch_size = 128 diff --git a/dopamine/agents/dqn/configs/dqn_icml.gin b/dopamine/agents/dqn/configs/dqn_icml.gin index eea9796..b68fa94 100644 --- a/dopamine/agents/dqn/configs/dqn_icml.gin +++ b/dopamine/agents/dqn/configs/dqn_icml.gin @@ -1,37 +1,37 @@ -# Hyperparameters used for reporting DQN results in Bellemare et al. (2017). -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer -import gin.tf.external_configurables - -DQNAgent.gamma = 0.99 -DQNAgent.update_horizon = 1 -DQNAgent.min_replay_history = 50000 # agent steps -DQNAgent.update_period = 4 -DQNAgent.target_update_period = 10000 # agent steps -DQNAgent.epsilon_train = 0.01 -DQNAgent.epsilon_eval = 0.001 -DQNAgent.epsilon_decay_period = 1000000 # agent steps -DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -DQNAgent.optimizer = @tf.train.RMSPropOptimizer() - -tf.train.RMSPropOptimizer.learning_rate = 0.00025 -tf.train.RMSPropOptimizer.decay = 0.95 -tf.train.RMSPropOptimizer.momentum = 0.0 -tf.train.RMSPropOptimizer.epsilon = 0.00001 -tf.train.RMSPropOptimizer.centered = True - -atari_lib.create_atari_environment.game_name = 'Pong' -# Deterministic ALE version used in the DQN Nature paper (Mnih et al., 2015). -atari_lib.create_atari_environment.sticky_actions = False -create_agent.agent_name = 'dqn' -Runner.num_iterations = 200 -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps - -AtariPreprocessing.terminal_on_life_loss = True - -WrappedReplayBuffer.replay_capacity = 1000000 -WrappedReplayBuffer.batch_size = 32 +# Hyperparameters used for reporting DQN results in Bellemare et al. (2017). +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.agents.dqn.dqn_agent +import dopamine.replay_memory.circular_replay_buffer +import gin.tf.external_configurables + +DQNAgent.gamma = 0.99 +DQNAgent.update_horizon = 1 +DQNAgent.min_replay_history = 50000 # agent steps +DQNAgent.update_period = 4 +DQNAgent.target_update_period = 10000 # agent steps +DQNAgent.epsilon_train = 0.01 +DQNAgent.epsilon_eval = 0.001 +DQNAgent.epsilon_decay_period = 1000000 # agent steps +DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +DQNAgent.optimizer = @tf.train.RMSPropOptimizer() + +tf.train.RMSPropOptimizer.learning_rate = 0.00025 +tf.train.RMSPropOptimizer.decay = 0.95 +tf.train.RMSPropOptimizer.momentum = 0.0 +tf.train.RMSPropOptimizer.epsilon = 0.00001 +tf.train.RMSPropOptimizer.centered = True + +atari_lib.create_atari_environment.game_name = 'Pong' +# Deterministic ALE version used in the DQN Nature paper (Mnih et al., 2015). +atari_lib.create_atari_environment.sticky_actions = False +create_agent.agent_name = 'dqn' +Runner.num_iterations = 200 +Runner.training_steps = 250000 # agent steps +Runner.evaluation_steps = 125000 # agent steps +Runner.max_steps_per_episode = 27000 # agent steps + +AtariPreprocessing.terminal_on_life_loss = True + +WrappedReplayBuffer.replay_capacity = 1000000 +WrappedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/dqn/configs/dqn_nature.gin b/dopamine/agents/dqn/configs/dqn_nature.gin index f33109f..54c4ed7 100644 --- a/dopamine/agents/dqn/configs/dqn_nature.gin +++ b/dopamine/agents/dqn/configs/dqn_nature.gin @@ -1,41 +1,41 @@ -# Hyperparameters used in Mnih et al. (2015). -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer -import gin.tf.external_configurables - -DQNAgent.gamma = 0.99 -DQNAgent.update_horizon = 1 -DQNAgent.runtype = 'RUNTYPE' -DQNAgent.game = 'GAME' -DQNAgent.min_replay_history = 50000 # agent steps -DQNAgent.update_period = 4 -DQNAgent.target_update_period = 10000 # agent steps -DQNAgent.epsilon_train = 0.1 -DQNAgent.epsilon_eval = 0.05 -DQNAgent.epsilon_decay_period = 1000000 # agent steps -DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -DQNAgent.optimizer = @tf.train.RMSPropOptimizer() - -tf.train.RMSPropOptimizer.learning_rate = 0.00025 -tf.train.RMSPropOptimizer.decay = 0.95 -tf.train.RMSPropOptimizer.momentum = 0.0 -tf.train.RMSPropOptimizer.epsilon = 0.00001 -tf.train.RMSPropOptimizer.centered = True - -atari_lib.create_atari_environment.game_name = 'GAME' -# Deterministic ALE version used in the DQN Nature paper (Mnih et al., 2015). -atari_lib.create_atari_environment.sticky_actions = False -create_agent.agent_name = 'dqn' -Runner.game = 'GAME' -Runner.runtype = 'RUNTYPE' -Runner.num_iterations = 200 -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps - -AtariPreprocessing.terminal_on_life_loss = True - -WrappedReplayBuffer.replay_capacity = 1000000 -WrappedReplayBuffer.batch_size = 32 +# Hyperparameters used in Mnih et al. (2015). +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.agents.dqn.dqn_agent +import dopamine.replay_memory.circular_replay_buffer +import gin.tf.external_configurables + +DQNAgent.gamma = 0.99 +DQNAgent.update_horizon = 1 +DQNAgent.runtype = 'RUNTYPE' +DQNAgent.game = 'GAME' +DQNAgent.min_replay_history = 50000 # agent steps +DQNAgent.update_period = 4 +DQNAgent.target_update_period = 10000 # agent steps +DQNAgent.epsilon_train = 0.1 +DQNAgent.epsilon_eval = 0.05 +DQNAgent.epsilon_decay_period = 1000000 # agent steps +DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +DQNAgent.optimizer = @tf.train.RMSPropOptimizer() + +tf.train.RMSPropOptimizer.learning_rate = 0.00025 +tf.train.RMSPropOptimizer.decay = 0.95 +tf.train.RMSPropOptimizer.momentum = 0.0 +tf.train.RMSPropOptimizer.epsilon = 0.00001 +tf.train.RMSPropOptimizer.centered = True + +atari_lib.create_atari_environment.game_name = 'GAME' +# Deterministic ALE version used in the DQN Nature paper (Mnih et al., 2015). +atari_lib.create_atari_environment.sticky_actions = False +create_agent.agent_name = 'dqn' +Runner.game = 'GAME' +Runner.runtype = 'RUNTYPE' +Runner.num_iterations = 200 +Runner.training_steps = 250000 # agent steps +Runner.evaluation_steps = 125000 # agent steps +Runner.max_steps_per_episode = 27000 # agent steps + +AtariPreprocessing.terminal_on_life_loss = True + +WrappedReplayBuffer.replay_capacity = 1000000 +WrappedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/dqn/dqn_agent.py b/dopamine/agents/dqn/dqn_agent.py index 9edf61b..ed66cfe 100644 --- a/dopamine/agents/dqn/dqn_agent.py +++ b/dopamine/agents/dqn/dqn_agent.py @@ -1,573 +1,573 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Compact implementation of a DQN agent.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import math -import os -import random -import pickle - - - -from dopamine.discrete_domains import atari_lib -from dopamine.replay_memory import circular_replay_buffer -import numpy as np -import tensorflow as tf - -import gin.tf - -slim = tf.contrib.slim - - -# These are aliases which are used by other classes. -NATURE_DQN_OBSERVATION_SHAPE = atari_lib.NATURE_DQN_OBSERVATION_SHAPE -NATURE_DQN_DTYPE = atari_lib.NATURE_DQN_DTYPE -NATURE_DQN_STACK_SIZE = atari_lib.NATURE_DQN_STACK_SIZE -nature_dqn_network = atari_lib.nature_dqn_network - - -@gin.configurable -def linearly_decaying_epsilon(decay_period, step, warmup_steps, epsilon): - """Returns the current epsilon for the agent's epsilon-greedy policy. - - This follows the Nature DQN schedule of a linearly decaying epsilon (Mnih et - al., 2015). The schedule is as follows: - Begin at 1. until warmup_steps steps have been taken; then - Linearly decay epsilon from 1. to epsilon in decay_period steps; and then - Use epsilon from there on. - - Args: - decay_period: float, the period over which epsilon is decayed. - step: int, the number of training steps completed so far. - warmup_steps: int, the number of steps taken before epsilon is decayed. - epsilon: float, the final value to which to decay the epsilon parameter. - - Returns: - A float, the current epsilon value computed according to the schedule. - """ - steps_left = decay_period + warmup_steps - step - bonus = (1.0 - epsilon) * steps_left / decay_period - bonus = np.clip(bonus, 0., 1. - epsilon) - return epsilon + bonus - - -@gin.configurable -def identity_epsilon(unused_decay_period, unused_step, unused_warmup_steps, - epsilon): - return epsilon - - -@gin.configurable -class DQNAgent(object): - """An implementation of the DQN agent.""" - - def __init__(self, - sess, - num_actions, - observation_shape=atari_lib.NATURE_DQN_OBSERVATION_SHAPE, - observation_dtype=atari_lib.NATURE_DQN_DTYPE, - stack_size=atari_lib.NATURE_DQN_STACK_SIZE, - network=atari_lib.nature_dqn_network, - gamma=0.99, - runtype='', - game='', - update_horizon=1, - min_replay_history=20000, - update_period=4, - target_update_period=8000, - epsilon_fn=linearly_decaying_epsilon, - epsilon_train=0.01, - epsilon_eval=0.001, - epsilon_decay_period=250000, - tf_device='/cpu:*', - use_staging=True, - max_tf_checkpoints_to_keep=4, - optimizer=tf.train.RMSPropOptimizer( - learning_rate=0.00025, - decay=0.95, - momentum=0.0, - epsilon=0.00001, - centered=True), - summary_writer=None, - summary_writing_frequency=500): - """Initializes the agent and constructs the components of its graph. - - Args: - sess: `tf.Session`, for executing ops. - num_actions: int, number of actions the agent can take at any state. - observation_shape: tuple of ints describing the observation shape. - observation_dtype: tf.DType, specifies the type of the observations. Note - that if your inputs are continuous, you should set this to tf.float32. - stack_size: int, number of frames to use in state stack. - network: function expecting three parameters: - (num_actions, network_type, state). This function will return the - network_type object containing the tensors output by the network. - See dopamine.discrete_domains.atari_lib.nature_dqn_network as - an example. - gamma: float, discount factor with the usual RL meaning. - update_horizon: int, horizon at which updates are performed, the 'n' in - n-step update. - min_replay_history: int, number of transitions that should be experienced - before the agent begins training its value function. - update_period: int, period between DQN updates. - target_update_period: int, update period for the target network. - epsilon_fn: function expecting 4 parameters: - (decay_period, step, warmup_steps, epsilon). This function should return - the epsilon value used for exploration during training. - epsilon_train: float, the value to which the agent's epsilon is eventually - decayed during training. - epsilon_eval: float, epsilon used when evaluating the agent. - epsilon_decay_period: int, length of the epsilon decay schedule. - tf_device: str, Tensorflow device on which the agent's graph is executed. - use_staging: bool, when True use a staging area to prefetch the next - training batch, speeding training up by about 30%. - max_tf_checkpoints_to_keep: int, the number of TensorFlow checkpoints to - keep. - optimizer: `tf.train.Optimizer`, for training the value function. - summary_writer: SummaryWriter object for outputting training statistics. - Summary writing disabled if set to None. - summary_writing_frequency: int, frequency with which summaries will be - written. Lower values will result in slower training. - """ - assert isinstance(observation_shape, tuple) - tf.logging.info('Creating %s agent with the following parameters:', - self.__class__.__name__) - tf.logging.info('\t gamma: %f', gamma) - tf.logging.info('\t update_horizon: %f', update_horizon) - tf.logging.info('\t min_replay_history: %d', min_replay_history) - tf.logging.info('\t update_period: %d', update_period) - tf.logging.info('\t target_update_period: %d', target_update_period) - tf.logging.info('\t epsilon_train: %f', epsilon_train) - tf.logging.info('\t epsilon_eval: %f', epsilon_eval) - tf.logging.info('\t epsilon_decay_period: %d', epsilon_decay_period) - tf.logging.info('\t tf_device: %s', tf_device) - tf.logging.info('\t use_staging: %s', use_staging) - tf.logging.info('\t optimizer: %s', optimizer) - - self.num_actions = num_actions - self.observation_shape = tuple(observation_shape) - self.observation_dtype = observation_dtype - self.stack_size = stack_size - self.network = network - self.gamma = gamma - self.update_horizon = update_horizon - self.cumulative_gamma = math.pow(gamma, update_horizon) - self.min_replay_history = min_replay_history - self.target_update_period = target_update_period - self.epsilon_fn = epsilon_fn - self.epsilon_train = epsilon_train - self.epsilon_eval = epsilon_eval - self.epsilon_decay_period = epsilon_decay_period - self.update_period = update_period - self.eval_mode = False - self.training_steps = 0 - self.optimizer = optimizer - self.summary_writer = summary_writer - self.summary_writing_frequency = summary_writing_frequency - self.runtype = runtype - self.game = game - self.filename = './fout-visual/aux-%s_%s.pkl' % (self.game, self.runtype) - self.dict1 = {"training_step": [], - "info": []} - if runtype is not None and 'aux' in runtype: - self.auxfactor = float(runtype[3:]) - else: - self.auxfactor = 0 - - with tf.device(tf_device): - # Create a placeholder for the state input to the DQN network. - # The last axis indicates the number of consecutive frames stacked. - state_shape = (1,) + self.observation_shape + (stack_size,) - self.state = np.zeros(state_shape) - self.state_ph = tf.placeholder(self.observation_dtype, state_shape, - name='state_ph') - self._replay = self._build_replay_buffer(use_staging) - - self._build_networks() - - self._train_op = self._build_train_op() - self._sync_qt_ops = self._build_sync_op() - - if self.summary_writer is not None: - # All tf.summaries should have been defined prior to running this. - self._merged_summaries = tf.summary.merge_all() - self._sess = sess - self._saver = tf.train.Saver(max_to_keep=max_tf_checkpoints_to_keep) - - # Variables to be initialized by the agent once it interacts with the - # environment. - self._observation = None - self._last_observation = None - - def _get_network_type(self): - """Returns the type of the outputs of a Q value network. - - Returns: - net_type: _network_type object defining the outputs of the network. - """ - return collections.namedtuple('DQN_network', ['q_values', 'B_values']) - - def _network_template(self, state, next_state): - """Builds the convolutional network used to compute the agent's Q-values. - - Args: - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - return self.network(self.num_actions, self._get_network_type(), state, True, next_state) - - def _build_networks(self): - """Builds the Q-value network computations needed for acting and training. - - These are: - self.online_convnet: For computing the current state's Q-values. - self.target_convnet: For computing the next state's target Q-values. - self._net_outputs: The actual Q-values. - self._q_argmax: The action maximizing the current state's Q-values. - self._replay_net_outputs: The replayed states' Q-values. - self._replay_next_target_net_outputs: The replayed next states' target - Q-values (see Mnih et al., 2015 for details). - """ - # Calling online_convnet will generate a new graph as defined in - # self._get_network_template using whatever input is passed, but will always - # share the same weights. - self.online_convnet = tf.make_template('Online', self._network_template) - self.target_convnet = tf.make_template('Target', self._network_template) - if 'aux' in self.runtype: - self._net_outputs = self.online_convnet(self.state_ph, self.state_ph) - else: - self._net_outputs = self.online_convnet(self.state_ph) - # TODO(bellemare): Ties should be broken. They are unlikely to happen when - # using a deep network, but may affect performance with a linear - # approximation scheme. - self._q_argmax = tf.argmax(self._net_outputs.q_values, axis=1)[0] - - if 'aux' in self.runtype: - self._replay_net_outputs = self.online_convnet(self._replay.states, self._replay.next_states) - self._replay_next_target_net_outputs = self.target_convnet( - self._replay.next_states, self._replay.next_states) - else: - self._replay_net_outputs = self.online_convnet(self._replay.states) - self._replay_next_target_net_outputs = self.target_convnet( - self._replay.next_states) - - def _build_replay_buffer(self, use_staging): - """Creates the replay buffer used by the agent. - - Args: - use_staging: bool, if True, uses a staging area to prefetch data for - faster training. - - Returns: - A WrapperReplayBuffer object. - """ - return circular_replay_buffer.WrappedReplayBuffer( - observation_shape=self.observation_shape, - stack_size=self.stack_size, - use_staging=use_staging, - update_horizon=self.update_horizon, - gamma=self.gamma, - observation_dtype=self.observation_dtype.as_numpy_dtype) - - def _build_target_q_op(self): - """Build an op used as a target for the Q-value. - - Returns: - target_q_op: An op calculating the Q-value. - """ - # Get the maximum Q-value across the actions dimension. - replay_next_qt_max = tf.reduce_max( - self._replay_next_target_net_outputs.q_values, 1) - # Calculate the Bellman target value. - # Q_t = R_t + \gamma^N * Q'_t+1 - # where, - # Q'_t+1 = \argmax_a Q(S_t+1, a) - # (or) 0 if S_t is a terminal state, - # and - # N is the update horizon (by default, N=1). - return self._replay.rewards + self.cumulative_gamma * replay_next_qt_max * ( - 1. - tf.cast(self._replay.terminals, tf.float32)) - - def _build_train_op(self): - """Builds a training op. - - Returns: - train_op: An op performing one step of training from replay data. - """ - replay_action_one_hot = tf.one_hot( - self._replay.actions, self.num_actions, 1., 0., name='action_one_hot') - replay_chosen_q = tf.reduce_sum( - self._replay_net_outputs.q_values * replay_action_one_hot, - reduction_indices=1, - name='replay_chosen_q') - if 'aux' in self.runtype: - replay_chosen_B = tf.reduce_sum( - self._replay_net_outputs.B_values * replay_action_one_hot, - reduction_indices=1, - name='replay_chosen_B') - - target = tf.stop_gradient(self._build_target_q_op()) - if self.auxfactor > 0: - loss = tf.losses.huber_loss( - target, replay_chosen_q + replay_chosen_B, reduction=tf.losses.Reduction.NONE) - loss += self.auxfactor * tf.square(replay_chosen_B) - else: - loss = tf.losses.huber_loss( - target, replay_chosen_q, reduction=tf.losses.Reduction.NONE) - if self.summary_writer is not None: - with tf.variable_scope('Losses'): - tf.summary.scalar('HuberLoss', tf.reduce_mean(loss)) - if 'aux' in self.runtype: - return self.optimizer.minimize(tf.reduce_mean(loss)), replay_chosen_B, target - replay_chosen_q - else: - return self.optimizer.minimize(tf.reduce_mean(loss)) - - def _build_sync_op(self): - """Builds ops for assigning weights from online to target network. - - Returns: - ops: A list of ops assigning weights from online to target network. - """ - # Get trainable variables from online and target DQNs - sync_qt_ops = [] - trainables_online = tf.get_collection( - tf.GraphKeys.TRAINABLE_VARIABLES, scope='Online') - trainables_target = tf.get_collection( - tf.GraphKeys.TRAINABLE_VARIABLES, scope='Target') - for (w_online, w_target) in zip(trainables_online, trainables_target): - # Assign weights from online to target network. - sync_qt_ops.append(w_target.assign(w_online, use_locking=True)) - return sync_qt_ops - - def begin_episode(self, observation): - """Returns the agent's first action for this episode. - - Args: - observation: numpy array, the environment's initial observation. - - Returns: - int, the selected action. - """ - self._reset_state() - self._record_observation(observation) - - if not self.eval_mode: - self._train_step() - - self.action = self._select_action() - return self.action - - def step(self, reward, observation): - """Records the most recent transition and returns the agent's next action. - - We store the observation of the last time step since we want to store it - with the reward. - - Args: - reward: float, the reward received from the agent's most recent action. - observation: numpy array, the most recent observation. - - Returns: - int, the selected action. - """ - self._last_observation = self._observation - self._record_observation(observation) - - if not self.eval_mode: - self._store_transition(self._last_observation, self.action, reward, False) - self._train_step() - - self.action = self._select_action() - return self.action - - def end_episode(self, reward): - """Signals the end of the episode to the agent. - - We store the observation of the current time step, which is the last - observation of the episode. - - Args: - reward: float, the last reward from the environment. - """ - if not self.eval_mode: - self._store_transition(self._observation, self.action, reward, True) - - def _select_action(self): - """Select an action from the set of available actions. - - Chooses an action randomly with probability self._calculate_epsilon(), and - otherwise acts greedily according to the current Q-value estimates. - - Returns: - int, the selected action. - """ - if self.eval_mode: - epsilon = self.epsilon_eval - else: - epsilon = self.epsilon_fn( - self.epsilon_decay_period, - self.training_steps, - self.min_replay_history, - self.epsilon_train) - if random.random() <= epsilon: - # Choose a random action with probability epsilon. - return random.randint(0, self.num_actions - 1) - else: - # Choose the action with highest Q-value at the current state. - return self._sess.run(self._q_argmax, {self.state_ph: self.state}) - - def _train_step(self): - """Runs a single training step. - - Runs a training op if both: - (1) A minimum number of frames have been added to the replay buffer. - (2) `training_steps` is a multiple of `update_period`. - - Also, syncs weights from online to target network if training steps is a - multiple of target update period. - """ - # Run a train op at the rate of self.update_period if enough training steps - # have been run. This matches the Nature DQN behaviour. - if self._replay.memory.add_count > self.min_replay_history: - if self.training_steps % self.update_period == 0: - if 'aux' in self.runtype: - _, B, delta = self._sess.run(self._train_op) - if self.training_steps % 10000 == 0: - print (self.training_steps, B[0], delta[0]) - self.dict1["training_step"].append(self.training_steps) - self.dict1["info"].append([B, delta]) - with open(self.filename, 'wb') as handle: - pickle.dump(self.dict1, handle, protocol=pickle.HIGHEST_PROTOCOL) - else: - self._sess.run(self._train_op) - if (self.summary_writer is not None and - self.training_steps > 0 and - self.training_steps % self.summary_writing_frequency == 0): - summary = self._sess.run(self._merged_summaries) - self.summary_writer.add_summary(summary, self.training_steps) - - if self.training_steps % self.target_update_period == 0: - self._sess.run(self._sync_qt_ops) - - self.training_steps += 1 - - def _record_observation(self, observation): - """Records an observation and update state. - - Extracts a frame from the observation vector and overwrites the oldest - frame in the state buffer. - - Args: - observation: numpy array, an observation from the environment. - """ - # Set current observation. We do the reshaping to handle environments - # without frame stacking. - self._observation = np.reshape(observation, self.observation_shape) - # Swap out the oldest frame with the current frame. - self.state = np.roll(self.state, -1, axis=-1) - self.state[0, ..., -1] = self._observation - - def _store_transition(self, last_observation, action, reward, is_terminal): - """Stores an experienced transition. - - Executes a tf session and executes replay buffer ops in order to store the - following tuple in the replay buffer: - (last_observation, action, reward, is_terminal). - - Pedantically speaking, this does not actually store an entire transition - since the next state is recorded on the following time step. - - Args: - last_observation: numpy array, last observation. - action: int, the action taken. - reward: float, the reward. - is_terminal: bool, indicating if the current state is a terminal state. - """ - self._replay.add(last_observation, action, reward, is_terminal) - - def _reset_state(self): - """Resets the agent state by filling it with zeros.""" - self.state.fill(0) - - def bundle_and_checkpoint(self, checkpoint_dir, iteration_number): - """Returns a self-contained bundle of the agent's state. - - This is used for checkpointing. It will return a dictionary containing all - non-TensorFlow objects (to be saved into a file by the caller), and it saves - all TensorFlow objects into a checkpoint file. - - Args: - checkpoint_dir: str, directory where TensorFlow objects will be saved. - iteration_number: int, iteration number to use for naming the checkpoint - file. - - Returns: - A dict containing additional Python objects to be checkpointed by the - experiment. If the checkpoint directory does not exist, returns None. - """ - if not tf.gfile.Exists(checkpoint_dir): - return None - # Call the Tensorflow saver to checkpoint the graph. - self._saver.save( - self._sess, - os.path.join(checkpoint_dir, 'tf_ckpt'), - global_step=iteration_number) - # Checkpoint the out-of-graph replay buffer. - self._replay.save(checkpoint_dir, iteration_number) - bundle_dictionary = {} - bundle_dictionary['state'] = self.state - bundle_dictionary['eval_mode'] = self.eval_mode - bundle_dictionary['training_steps'] = self.training_steps - return bundle_dictionary - - def unbundle(self, checkpoint_dir, iteration_number, bundle_dictionary): - """Restores the agent from a checkpoint. - - Restores the agent's Python objects to those specified in bundle_dictionary, - and restores the TensorFlow objects to those specified in the - checkpoint_dir. If the checkpoint_dir does not exist, will not reset the - agent's state. - - Args: - checkpoint_dir: str, path to the checkpoint saved by tf.Save. - iteration_number: int, checkpoint version, used when restoring replay - buffer. - bundle_dictionary: dict, containing additional Python objects owned by - the agent. - - Returns: - bool, True if unbundling was successful. - """ - try: - # self._replay.load() will throw a NotFoundError if it does not find all - # the necessary files, in which case we abort the process & return False. - self._replay.load(checkpoint_dir, iteration_number) - except tf.errors.NotFoundError: - return False - for key in self.__dict__: - if key in bundle_dictionary: - self.__dict__[key] = bundle_dictionary[key] - # Restore the agent's TensorFlow graph. - self._saver.restore(self._sess, - os.path.join(checkpoint_dir, - 'tf_ckpt-{}'.format(iteration_number))) - return True +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Compact implementation of a DQN agent.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import math +import os +import random +import pickle + + + +from dopamine.discrete_domains import atari_lib +from dopamine.replay_memory import circular_replay_buffer +import numpy as np +import tensorflow as tf + +import gin.tf + +slim = tf.contrib.slim + + +# These are aliases which are used by other classes. +NATURE_DQN_OBSERVATION_SHAPE = atari_lib.NATURE_DQN_OBSERVATION_SHAPE +NATURE_DQN_DTYPE = atari_lib.NATURE_DQN_DTYPE +NATURE_DQN_STACK_SIZE = atari_lib.NATURE_DQN_STACK_SIZE +nature_dqn_network = atari_lib.nature_dqn_network + + +@gin.configurable +def linearly_decaying_epsilon(decay_period, step, warmup_steps, epsilon): + """Returns the current epsilon for the agent's epsilon-greedy policy. + + This follows the Nature DQN schedule of a linearly decaying epsilon (Mnih et + al., 2015). The schedule is as follows: + Begin at 1. until warmup_steps steps have been taken; then + Linearly decay epsilon from 1. to epsilon in decay_period steps; and then + Use epsilon from there on. + + Args: + decay_period: float, the period over which epsilon is decayed. + step: int, the number of training steps completed so far. + warmup_steps: int, the number of steps taken before epsilon is decayed. + epsilon: float, the final value to which to decay the epsilon parameter. + + Returns: + A float, the current epsilon value computed according to the schedule. + """ + steps_left = decay_period + warmup_steps - step + bonus = (1.0 - epsilon) * steps_left / decay_period + bonus = np.clip(bonus, 0., 1. - epsilon) + return epsilon + bonus + + +@gin.configurable +def identity_epsilon(unused_decay_period, unused_step, unused_warmup_steps, + epsilon): + return epsilon + + +@gin.configurable +class DQNAgent(object): + """An implementation of the DQN agent.""" + + def __init__(self, + sess, + num_actions, + observation_shape=atari_lib.NATURE_DQN_OBSERVATION_SHAPE, + observation_dtype=atari_lib.NATURE_DQN_DTYPE, + stack_size=atari_lib.NATURE_DQN_STACK_SIZE, + network=atari_lib.nature_dqn_network, + gamma=0.99, + runtype='', + game='', + update_horizon=1, + min_replay_history=20000, + update_period=4, + target_update_period=8000, + epsilon_fn=linearly_decaying_epsilon, + epsilon_train=0.01, + epsilon_eval=0.001, + epsilon_decay_period=250000, + tf_device='/cpu:*', + use_staging=True, + max_tf_checkpoints_to_keep=4, + optimizer=tf.train.RMSPropOptimizer( + learning_rate=0.00025, + decay=0.95, + momentum=0.0, + epsilon=0.00001, + centered=True), + summary_writer=None, + summary_writing_frequency=500): + """Initializes the agent and constructs the components of its graph. + + Args: + sess: `tf.Session`, for executing ops. + num_actions: int, number of actions the agent can take at any state. + observation_shape: tuple of ints describing the observation shape. + observation_dtype: tf.DType, specifies the type of the observations. Note + that if your inputs are continuous, you should set this to tf.float32. + stack_size: int, number of frames to use in state stack. + network: function expecting three parameters: + (num_actions, network_type, state). This function will return the + network_type object containing the tensors output by the network. + See dopamine.discrete_domains.atari_lib.nature_dqn_network as + an example. + gamma: float, discount factor with the usual RL meaning. + update_horizon: int, horizon at which updates are performed, the 'n' in + n-step update. + min_replay_history: int, number of transitions that should be experienced + before the agent begins training its value function. + update_period: int, period between DQN updates. + target_update_period: int, update period for the target network. + epsilon_fn: function expecting 4 parameters: + (decay_period, step, warmup_steps, epsilon). This function should return + the epsilon value used for exploration during training. + epsilon_train: float, the value to which the agent's epsilon is eventually + decayed during training. + epsilon_eval: float, epsilon used when evaluating the agent. + epsilon_decay_period: int, length of the epsilon decay schedule. + tf_device: str, Tensorflow device on which the agent's graph is executed. + use_staging: bool, when True use a staging area to prefetch the next + training batch, speeding training up by about 30%. + max_tf_checkpoints_to_keep: int, the number of TensorFlow checkpoints to + keep. + optimizer: `tf.train.Optimizer`, for training the value function. + summary_writer: SummaryWriter object for outputting training statistics. + Summary writing disabled if set to None. + summary_writing_frequency: int, frequency with which summaries will be + written. Lower values will result in slower training. + """ + assert isinstance(observation_shape, tuple) + tf.logging.info('Creating %s agent with the following parameters:', + self.__class__.__name__) + tf.logging.info('\t gamma: %f', gamma) + tf.logging.info('\t update_horizon: %f', update_horizon) + tf.logging.info('\t min_replay_history: %d', min_replay_history) + tf.logging.info('\t update_period: %d', update_period) + tf.logging.info('\t target_update_period: %d', target_update_period) + tf.logging.info('\t epsilon_train: %f', epsilon_train) + tf.logging.info('\t epsilon_eval: %f', epsilon_eval) + tf.logging.info('\t epsilon_decay_period: %d', epsilon_decay_period) + tf.logging.info('\t tf_device: %s', tf_device) + tf.logging.info('\t use_staging: %s', use_staging) + tf.logging.info('\t optimizer: %s', optimizer) + + self.num_actions = num_actions + self.observation_shape = tuple(observation_shape) + self.observation_dtype = observation_dtype + self.stack_size = stack_size + self.network = network + self.gamma = gamma + self.update_horizon = update_horizon + self.cumulative_gamma = math.pow(gamma, update_horizon) + self.min_replay_history = min_replay_history + self.target_update_period = target_update_period + self.epsilon_fn = epsilon_fn + self.epsilon_train = epsilon_train + self.epsilon_eval = epsilon_eval + self.epsilon_decay_period = epsilon_decay_period + self.update_period = update_period + self.eval_mode = False + self.training_steps = 0 + self.optimizer = optimizer + self.summary_writer = summary_writer + self.summary_writing_frequency = summary_writing_frequency + self.runtype = runtype + self.game = game + self.filename = './fout-visual/aux-%s_%s.pkl' % (self.game, self.runtype) + self.dict1 = {"training_step": [], + "info": []} + if runtype is not None and 'aux' in runtype: + self.auxfactor = float(runtype[3:]) + else: + self.auxfactor = 0 + + with tf.device(tf_device): + # Create a placeholder for the state input to the DQN network. + # The last axis indicates the number of consecutive frames stacked. + state_shape = (1,) + self.observation_shape + (stack_size,) + self.state = np.zeros(state_shape) + self.state_ph = tf.placeholder(self.observation_dtype, state_shape, + name='state_ph') + self._replay = self._build_replay_buffer(use_staging) + + self._build_networks() + + self._train_op = self._build_train_op() + self._sync_qt_ops = self._build_sync_op() + + if self.summary_writer is not None: + # All tf.summaries should have been defined prior to running this. + self._merged_summaries = tf.summary.merge_all() + self._sess = sess + self._saver = tf.train.Saver(max_to_keep=max_tf_checkpoints_to_keep) + + # Variables to be initialized by the agent once it interacts with the + # environment. + self._observation = None + self._last_observation = None + + def _get_network_type(self): + """Returns the type of the outputs of a Q value network. + + Returns: + net_type: _network_type object defining the outputs of the network. + """ + return collections.namedtuple('DQN_network', ['q_values', 'B_values']) + + def _network_template(self, state, next_state): + """Builds the convolutional network used to compute the agent's Q-values. + + Args: + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + return self.network(self.num_actions, self._get_network_type(), state, True, next_state) + + def _build_networks(self): + """Builds the Q-value network computations needed for acting and training. + + These are: + self.online_convnet: For computing the current state's Q-values. + self.target_convnet: For computing the next state's target Q-values. + self._net_outputs: The actual Q-values. + self._q_argmax: The action maximizing the current state's Q-values. + self._replay_net_outputs: The replayed states' Q-values. + self._replay_next_target_net_outputs: The replayed next states' target + Q-values (see Mnih et al., 2015 for details). + """ + # Calling online_convnet will generate a new graph as defined in + # self._get_network_template using whatever input is passed, but will always + # share the same weights. + self.online_convnet = tf.make_template('Online', self._network_template) + self.target_convnet = tf.make_template('Target', self._network_template) + if 'aux' in self.runtype: + self._net_outputs = self.online_convnet(self.state_ph, self.state_ph) + else: + self._net_outputs = self.online_convnet(self.state_ph) + # TODO(bellemare): Ties should be broken. They are unlikely to happen when + # using a deep network, but may affect performance with a linear + # approximation scheme. + self._q_argmax = tf.argmax(self._net_outputs.q_values, axis=1)[0] + + if 'aux' in self.runtype: + self._replay_net_outputs = self.online_convnet(self._replay.states, self._replay.next_states) + self._replay_next_target_net_outputs = self.target_convnet( + self._replay.next_states, self._replay.next_states) + else: + self._replay_net_outputs = self.online_convnet(self._replay.states) + self._replay_next_target_net_outputs = self.target_convnet( + self._replay.next_states) + + def _build_replay_buffer(self, use_staging): + """Creates the replay buffer used by the agent. + + Args: + use_staging: bool, if True, uses a staging area to prefetch data for + faster training. + + Returns: + A WrapperReplayBuffer object. + """ + return circular_replay_buffer.WrappedReplayBuffer( + observation_shape=self.observation_shape, + stack_size=self.stack_size, + use_staging=use_staging, + update_horizon=self.update_horizon, + gamma=self.gamma, + observation_dtype=self.observation_dtype.as_numpy_dtype) + + def _build_target_q_op(self): + """Build an op used as a target for the Q-value. + + Returns: + target_q_op: An op calculating the Q-value. + """ + # Get the maximum Q-value across the actions dimension. + replay_next_qt_max = tf.reduce_max( + self._replay_next_target_net_outputs.q_values, 1) + # Calculate the Bellman target value. + # Q_t = R_t + \gamma^N * Q'_t+1 + # where, + # Q'_t+1 = \argmax_a Q(S_t+1, a) + # (or) 0 if S_t is a terminal state, + # and + # N is the update horizon (by default, N=1). + return self._replay.rewards + self.cumulative_gamma * replay_next_qt_max * ( + 1. - tf.cast(self._replay.terminals, tf.float32)) + + def _build_train_op(self): + """Builds a training op. + + Returns: + train_op: An op performing one step of training from replay data. + """ + replay_action_one_hot = tf.one_hot( + self._replay.actions, self.num_actions, 1., 0., name='action_one_hot') + replay_chosen_q = tf.reduce_sum( + self._replay_net_outputs.q_values * replay_action_one_hot, + reduction_indices=1, + name='replay_chosen_q') + if 'aux' in self.runtype: + replay_chosen_B = tf.reduce_sum( + self._replay_net_outputs.B_values * replay_action_one_hot, + reduction_indices=1, + name='replay_chosen_B') + + target = tf.stop_gradient(self._build_target_q_op()) + if self.auxfactor > 0: + loss = tf.losses.huber_loss( + target, replay_chosen_q + replay_chosen_B, reduction=tf.losses.Reduction.NONE) + loss += self.auxfactor * tf.square(replay_chosen_B) + else: + loss = tf.losses.huber_loss( + target, replay_chosen_q, reduction=tf.losses.Reduction.NONE) + if self.summary_writer is not None: + with tf.variable_scope('Losses'): + tf.summary.scalar('HuberLoss', tf.reduce_mean(loss)) + if 'aux' in self.runtype: + return self.optimizer.minimize(tf.reduce_mean(loss)), replay_chosen_B, target - replay_chosen_q + else: + return self.optimizer.minimize(tf.reduce_mean(loss)) + + def _build_sync_op(self): + """Builds ops for assigning weights from online to target network. + + Returns: + ops: A list of ops assigning weights from online to target network. + """ + # Get trainable variables from online and target DQNs + sync_qt_ops = [] + trainables_online = tf.get_collection( + tf.GraphKeys.TRAINABLE_VARIABLES, scope='Online') + trainables_target = tf.get_collection( + tf.GraphKeys.TRAINABLE_VARIABLES, scope='Target') + for (w_online, w_target) in zip(trainables_online, trainables_target): + # Assign weights from online to target network. + sync_qt_ops.append(w_target.assign(w_online, use_locking=True)) + return sync_qt_ops + + def begin_episode(self, observation): + """Returns the agent's first action for this episode. + + Args: + observation: numpy array, the environment's initial observation. + + Returns: + int, the selected action. + """ + self._reset_state() + self._record_observation(observation) + + if not self.eval_mode: + self._train_step() + + self.action = self._select_action() + return self.action + + def step(self, reward, observation): + """Records the most recent transition and returns the agent's next action. + + We store the observation of the last time step since we want to store it + with the reward. + + Args: + reward: float, the reward received from the agent's most recent action. + observation: numpy array, the most recent observation. + + Returns: + int, the selected action. + """ + self._last_observation = self._observation + self._record_observation(observation) + + if not self.eval_mode: + self._store_transition(self._last_observation, self.action, reward, False) + self._train_step() + + self.action = self._select_action() + return self.action + + def end_episode(self, reward): + """Signals the end of the episode to the agent. + + We store the observation of the current time step, which is the last + observation of the episode. + + Args: + reward: float, the last reward from the environment. + """ + if not self.eval_mode: + self._store_transition(self._observation, self.action, reward, True) + + def _select_action(self): + """Select an action from the set of available actions. + + Chooses an action randomly with probability self._calculate_epsilon(), and + otherwise acts greedily according to the current Q-value estimates. + + Returns: + int, the selected action. + """ + if self.eval_mode: + epsilon = self.epsilon_eval + else: + epsilon = self.epsilon_fn( + self.epsilon_decay_period, + self.training_steps, + self.min_replay_history, + self.epsilon_train) + if random.random() <= epsilon: + # Choose a random action with probability epsilon. + return random.randint(0, self.num_actions - 1) + else: + # Choose the action with highest Q-value at the current state. + return self._sess.run(self._q_argmax, {self.state_ph: self.state}) + + def _train_step(self): + """Runs a single training step. + + Runs a training op if both: + (1) A minimum number of frames have been added to the replay buffer. + (2) `training_steps` is a multiple of `update_period`. + + Also, syncs weights from online to target network if training steps is a + multiple of target update period. + """ + # Run a train op at the rate of self.update_period if enough training steps + # have been run. This matches the Nature DQN behaviour. + if self._replay.memory.add_count > self.min_replay_history: + if self.training_steps % self.update_period == 0: + if 'aux' in self.runtype: + _, B, delta = self._sess.run(self._train_op) + if self.training_steps % 10000 == 0: + print (self.training_steps, B[0], delta[0]) + self.dict1["training_step"].append(self.training_steps) + self.dict1["info"].append([B, delta]) + with open(self.filename, 'wb') as handle: + pickle.dump(self.dict1, handle, protocol=pickle.HIGHEST_PROTOCOL) + else: + self._sess.run(self._train_op) + if (self.summary_writer is not None and + self.training_steps > 0 and + self.training_steps % self.summary_writing_frequency == 0): + summary = self._sess.run(self._merged_summaries) + self.summary_writer.add_summary(summary, self.training_steps) + + if self.training_steps % self.target_update_period == 0: + self._sess.run(self._sync_qt_ops) + + self.training_steps += 1 + + def _record_observation(self, observation): + """Records an observation and update state. + + Extracts a frame from the observation vector and overwrites the oldest + frame in the state buffer. + + Args: + observation: numpy array, an observation from the environment. + """ + # Set current observation. We do the reshaping to handle environments + # without frame stacking. + self._observation = np.reshape(observation, self.observation_shape) + # Swap out the oldest frame with the current frame. + self.state = np.roll(self.state, -1, axis=-1) + self.state[0, ..., -1] = self._observation + + def _store_transition(self, last_observation, action, reward, is_terminal): + """Stores an experienced transition. + + Executes a tf session and executes replay buffer ops in order to store the + following tuple in the replay buffer: + (last_observation, action, reward, is_terminal). + + Pedantically speaking, this does not actually store an entire transition + since the next state is recorded on the following time step. + + Args: + last_observation: numpy array, last observation. + action: int, the action taken. + reward: float, the reward. + is_terminal: bool, indicating if the current state is a terminal state. + """ + self._replay.add(last_observation, action, reward, is_terminal) + + def _reset_state(self): + """Resets the agent state by filling it with zeros.""" + self.state.fill(0) + + def bundle_and_checkpoint(self, checkpoint_dir, iteration_number): + """Returns a self-contained bundle of the agent's state. + + This is used for checkpointing. It will return a dictionary containing all + non-TensorFlow objects (to be saved into a file by the caller), and it saves + all TensorFlow objects into a checkpoint file. + + Args: + checkpoint_dir: str, directory where TensorFlow objects will be saved. + iteration_number: int, iteration number to use for naming the checkpoint + file. + + Returns: + A dict containing additional Python objects to be checkpointed by the + experiment. If the checkpoint directory does not exist, returns None. + """ + if not tf.gfile.Exists(checkpoint_dir): + return None + # Call the Tensorflow saver to checkpoint the graph. + self._saver.save( + self._sess, + os.path.join(checkpoint_dir, 'tf_ckpt'), + global_step=iteration_number) + # Checkpoint the out-of-graph replay buffer. + self._replay.save(checkpoint_dir, iteration_number) + bundle_dictionary = {} + bundle_dictionary['state'] = self.state + bundle_dictionary['eval_mode'] = self.eval_mode + bundle_dictionary['training_steps'] = self.training_steps + return bundle_dictionary + + def unbundle(self, checkpoint_dir, iteration_number, bundle_dictionary): + """Restores the agent from a checkpoint. + + Restores the agent's Python objects to those specified in bundle_dictionary, + and restores the TensorFlow objects to those specified in the + checkpoint_dir. If the checkpoint_dir does not exist, will not reset the + agent's state. + + Args: + checkpoint_dir: str, path to the checkpoint saved by tf.Save. + iteration_number: int, checkpoint version, used when restoring replay + buffer. + bundle_dictionary: dict, containing additional Python objects owned by + the agent. + + Returns: + bool, True if unbundling was successful. + """ + try: + # self._replay.load() will throw a NotFoundError if it does not find all + # the necessary files, in which case we abort the process & return False. + self._replay.load(checkpoint_dir, iteration_number) + except tf.errors.NotFoundError: + return False + for key in self.__dict__: + if key in bundle_dictionary: + self.__dict__[key] = bundle_dictionary[key] + # Restore the agent's TensorFlow graph. + self._saver.restore(self._sess, + os.path.join(checkpoint_dir, + 'tf_ckpt-{}'.format(iteration_number))) + return True diff --git a/dopamine/agents/fqf/configs/fqf.gin b/dopamine/agents/fqf/configs/fqf.gin index 16daed9..3abb7bd 100644 --- a/dopamine/agents/fqf/configs/fqf.gin +++ b/dopamine/agents/fqf/configs/fqf.gin @@ -1,46 +1,46 @@ -# Hyperparameters follow Dabney et al. (2018). -import dopamine.agents.fqf.fqf_agent -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -FQFAgent.kappa = 1.0 -FQFAgent.num_tau_samples = 32 -FQFAgent.num_tau_prime_samples = 32 -FQFAgent.num_quantile_samples = 32 -FQFAgent.runtype = 'RUNTYPE' -FQFAgent.fqf_factor = 'FQFFACTOR' -FQFAgent.fqf_ent = 'FQFENT' -RainbowAgent.gamma = 0.99 -RainbowAgent.game = 'GAME' -RainbowAgent.runtype = 'RUNTYPE' -RainbowAgent.update_horizon = 1 -RainbowAgent.min_replay_history = 50000 # agent steps -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 10000 # agent steps -RainbowAgent.epsilon_train = 0.01 -RainbowAgent.epsilon_eval = 0.001 -RainbowAgent.epsilon_decay_period = 1000000 # agent steps -RainbowAgent.replay_scheme = 'uniform' -RainbowAgent.tf_device = '/gpu:0' # '/cpu:*' use for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.00005 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -atari_lib.create_atari_environment.game_name = 'GAME' -atari_lib.create_atari_environment.sticky_actions = False -create_agent.agent_name = 'implicit_quantile' -Runner.num_iterations = 200 -Runner.game = 'GAME' -Runner.runtype = 'RUNTYPE' -Runner.training_steps = 250000 -Runner.evaluation_steps = 125000 -Runner.max_steps_per_episode = 27000 - -AtariPreprocessing.terminal_on_life_loss = True - -WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 -WrappedPrioritizedReplayBuffer.batch_size = 32 +# Hyperparameters follow Dabney et al. (2018). +import dopamine.agents.fqf.fqf_agent +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +FQFAgent.kappa = 1.0 +FQFAgent.num_tau_samples = 32 +FQFAgent.num_tau_prime_samples = 32 +FQFAgent.num_quantile_samples = 32 +FQFAgent.runtype = 'RUNTYPE' +FQFAgent.fqf_factor = 'FQFFACTOR' +FQFAgent.fqf_ent = 'FQFENT' +RainbowAgent.gamma = 0.99 +RainbowAgent.game = 'GAME' +RainbowAgent.runtype = 'RUNTYPE' +RainbowAgent.update_horizon = 1 +RainbowAgent.min_replay_history = 50000 # agent steps +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 10000 # agent steps +RainbowAgent.epsilon_train = 0.01 +RainbowAgent.epsilon_eval = 0.001 +RainbowAgent.epsilon_decay_period = 1000000 # agent steps +RainbowAgent.replay_scheme = 'uniform' +RainbowAgent.tf_device = '/gpu:0' # '/cpu:*' use for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.00005 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +atari_lib.create_atari_environment.game_name = 'GAME' +atari_lib.create_atari_environment.sticky_actions = False +create_agent.agent_name = 'implicit_quantile' +Runner.num_iterations = 200 +Runner.game = 'GAME' +Runner.runtype = 'RUNTYPE' +Runner.training_steps = 250000 +Runner.evaluation_steps = 125000 +Runner.max_steps_per_episode = 27000 + +AtariPreprocessing.terminal_on_life_loss = True + +WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 +WrappedPrioritizedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/fqf/fqf_agent.py b/dopamine/agents/fqf/fqf_agent.py index 60a5913..c8e9720 100644 --- a/dopamine/agents/fqf/fqf_agent.py +++ b/dopamine/agents/fqf/fqf_agent.py @@ -1,410 +1,420 @@ -# coding=utf-8 -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import numpy as np - - - -from dopamine.agents.rainbow import rainbow_agent -from dopamine.discrete_domains import atari_lib -import tensorflow as tf - -import gin.tf - -slim = tf.contrib.slim - - -@gin.configurable -class FQFAgent(rainbow_agent.RainbowAgent): - - def __init__(self, - sess, - num_actions, - network=atari_lib.fqf_network, - kappa=1.0, - runtype=None, - fqf_factor=0.000001, - fqf_ent=0.001, - num_tau_samples=32, - num_tau_prime_samples=32, - num_quantile_samples=32, - quantile_embedding_dim=64, - double_dqn=False, - summary_writer=None, - summary_writing_frequency=500): - """Initializes the agent and constructs the Graph. - - Most of this constructor's parameters are IQN-specific hyperparameters whose - values are taken from Dabney et al. (2018). - - Args: - sess: `tf.Session` object for running associated ops. - num_actions: int, number of actions the agent can take at any state. - network: function expecting three parameters: - (num_actions, network_type, state). This function will return the - network_type object containing the tensors output by the network. - See dopamine.discrete_domains.atari_lib.nature_dqn_network as - an example. - kappa: float, Huber loss cutoff. - num_tau_samples: int, number of online quantile samples for loss - estimation. - num_tau_prime_samples: int, number of target quantile samples for loss - estimation. - num_quantile_samples: int, number of quantile samples for computing - Q-values. - quantile_embedding_dim: int, embedding dimension for the quantile input. - double_dqn: boolean, whether to perform double DQN style learning - as described in Van Hasselt et al.: https://arxiv.org/abs/1509.06461. - summary_writer: SummaryWriter object for outputting training statistics. - Summary writing disabled if set to None. - summary_writing_frequency: int, frequency with which summaries will be - written. Lower values will result in slower training. - """ - print ('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>') - self._runtype= runtype - print (self._runtype) - self.fqf_factor = float(fqf_factor) - self.ent = float(fqf_ent) - self.kappa = kappa - print ('fqf factor:', self.fqf_factor) - # num_tau_samples = N below equation (3) in the paper. - self.num_tau_samples = num_tau_samples - # num_tau_prime_samples = N' below equation (3) in the paper. - self.num_tau_prime_samples = num_tau_prime_samples - # num_quantile_samples = k below equation (3) in the paper. - self.num_quantile_samples = num_quantile_samples - # quantile_embedding_dim = n above equation (4) in the paper. - self.quantile_embedding_dim = quantile_embedding_dim - # option to perform double dqn. - self.double_dqn = double_dqn - if 'adam' in self._runtype: - self.optimizer1 = tf.train.AdamOptimizer( - learning_rate=0.00005 * self.fqf_factor, - epsilon=0.0003125) - else: - self.optimizer1 = tf.train.RMSPropOptimizer( - learning_rate=0.00005 * self.fqf_factor, - decay=0.95, - momentum=0.0, - epsilon=0.00001, - centered=True) - - super(FQFAgent, self).__init__( - sess=sess, - num_actions=num_actions, - network=network, - summary_writer=summary_writer, - summary_writing_frequency=summary_writing_frequency) - - def _get_network_type(self): - return collections.namedtuple( - 'iqn_network', ['quantile_values', 'quantiles', 'quantile_values_origin', 'quantiles_origin', 'Fv_diff', 'v_diff', 'quantile_values_mid', 'quantiles_mid', 'L_tau', 'gradient_tau', 'quantile_tau']) - - def _network_template(self, state, num_quantiles): - return self.network(self.num_actions, self.quantile_embedding_dim, - self._get_network_type(), state, num_quantiles, self._runtype) - - def _train_step(self): - """Runs a single training step. - - Runs a training op if both: - (1) A minimum number of frames have been added to the replay buffer. - (2) `training_steps` is a multiple of `update_period`. - - Also, syncs weights from online to target network if training steps is a - multiple of target update period. - """ - # Run a train op at the rate of self.update_period if enough training steps - # have been run. This matches the Nature DQN behaviour. - if self._replay.memory.add_count > self.min_replay_history: - if self.training_steps % self.update_period == 0: - _, _, _, loss, loss1, quan_value, quan, vdiff = self._sess.run(self._train_op) - if self.training_steps % 50000 == 0: - batchsize = 32 - quan_value = np.reshape(quan_value, [batchsize, self.num_tau_samples]) - quan = np.reshape(quan, [batchsize, self.num_tau_samples]) - quan_value = quan_value[0].tolist() - quan = quan[0].tolist() - vdiff = vdiff[:, 0].tolist() - print (">>> loss:", loss) - print (">>> loss1:", loss1) - print (">>> value:", quan_value) - print (">>> quans:", quan) - print (">>> vdiff:", vdiff) - print (">>> vdiff_sum:", np.sum(vdiff)) - if (self.summary_writer is not None and - self.training_steps > 0 and - self.training_steps % self.summary_writing_frequency == 0): - summary = self._sess.run(self._merged_summaries) - self.summary_writer.add_summary(summary, self.training_steps) - - if self.training_steps % self.target_update_period == 0: - self._sess.run(self._sync_qt_ops) - - self.training_steps += 1 - - def _build_networks(self): - """Builds the FQF computations needed for acting and training. - - These are: - self.online_convnet: For computing the current state's quantile values. - self.target_convnet: For computing the next state's target quantile - values. - self._net_outputs: The actual quantile values. - self._q_argmax: The action maximizing the current state's Q-values. - self._replay_net_outputs: The replayed states' quantile values. - self._replay_next_target_net_outputs: The replayed next states' target - quantile values. - """ - # Calling online_convnet will generate a new graph as defined in - # self._get_network_template using whatever input is passed, but will always - # share the same weights. - self.online_convnet = tf.make_template('Online', self._network_template) - self.target_convnet = tf.make_template('Target', self._network_template) - - # Compute the Q-values which are used for action selection in the current - # state. - self._net_outputs = self.online_convnet(self.state_ph, - self.num_quantile_samples) - # Shape of self._net_outputs.quantile_values: - # num_quantile_samples x num_actions. - # e.g. if num_actions is 2, it might look something like this: - # Vals for Quantile .2 Vals for Quantile .4 Vals for Quantile .6 - # [[0.1, 0.5], [0.15, -0.3], [0.15, -0.2]] - # Q-values = [(0.1 + 0.15 + 0.15)/3, (0.5 + 0.15 + -0.2)/3]. - if 'ws' in self._runtype: - self._q_values = tf.reduce_sum(self._net_outputs.quantile_values * self._net_outputs.v_diff, axis=0) #NOTE: quantile_values = quantile_values_mid - else: - self._q_values = tf.reduce_mean(self._net_outputs.quantile_values, axis=0) - self._q_argmax = tf.argmax(self._q_values, axis=0) - - self._replay_net_outputs = self.online_convnet(self._replay.states, - self.num_tau_samples) - # Shape: (num_tau_samples x batch_size) x num_actions. - self._replay_net_quantile_values = self._replay_net_outputs.quantile_values - self._replay_net_quantiles = self._replay_net_outputs.quantiles - - # Do the same for next states in the replay buffer. - self._replay_net_target_outputs = self.target_convnet( - self._replay.next_states, self.num_tau_prime_samples) - # Shape: (num_tau_prime_samples x batch_size) x num_actions. - vals = self._replay_net_target_outputs.quantile_values - self._replay_net_target_quantile_values = vals - - # Compute Q-values which are used for action selection for the next states - # in the replay buffer. Compute the argmax over the Q-values. - if self.double_dqn: - outputs_action = self.online_convnet(self._replay.next_states, - self.num_quantile_samples) - else: - outputs_action = self.target_convnet(self._replay.next_states, - self.num_quantile_samples) - - # Shape: (num_quantile_samples x batch_size) x num_actions. - target_quantile_values_action = outputs_action.quantile_values #NOTE: quantile_values = quantile_values_mid - # Shape: num_quantile_samples x batch_size x num_actions. - target_quantile_values_action = tf.reshape(target_quantile_values_action, - [self.num_quantile_samples, - self._replay.batch_size, - self.num_actions]) - # Shape: batch_size x num_actions. - if 'ws' in self._runtype: - v_diff = tf.reshape(outputs_action.v_diff, [self.num_quantile_samples, self._replay.batch_size, 1]) - self._replay_net_target_q_values = tf.squeeze(tf.reduce_sum( - target_quantile_values_action * v_diff, axis=0)) - else: - self._replay_net_target_q_values = tf.squeeze(tf.reduce_mean( - target_quantile_values_action, axis=0)) - self._replay_next_qt_argmax = tf.argmax( - self._replay_net_target_q_values, axis=1) - - def _build_target_quantile_values_op(self): - """Build an op used as a target for return values at given quantiles. - - Returns: - An op calculating the target quantile return. - """ - batch_size = tf.shape(self._replay.rewards)[0] - # Shape of rewards: (num_tau_prime_samples x batch_size) x 1. - rewards = self._replay.rewards[:, None] - rewards = tf.tile(rewards, [self.num_tau_prime_samples, 1]) - - is_terminal_multiplier = 1. - tf.to_float(self._replay.terminals) - # Incorporate terminal state to discount factor. - # size of gamma_with_terminal: (num_tau_prime_samples x batch_size) x 1. - gamma_with_terminal = self.cumulative_gamma * is_terminal_multiplier - gamma_with_terminal = tf.tile(gamma_with_terminal[:, None], - [self.num_tau_prime_samples, 1]) - - # Get the indices of the maximium Q-value across the action dimension. - # Shape of replay_next_qt_argmax: (num_tau_prime_samples x batch_size) x 1. - - replay_next_qt_argmax = tf.tile( - self._replay_next_qt_argmax[:, None], [self.num_tau_prime_samples, 1]) - - # Shape of batch_indices: (num_tau_prime_samples x batch_size) x 1. - batch_indices = tf.cast(tf.range( - self.num_tau_prime_samples * batch_size)[:, None], tf.int64) - - # Shape of batch_indexed_target_values: - # (num_tau_prime_samples x batch_size) x 2. - batch_indexed_target_values = tf.concat( - [batch_indices, replay_next_qt_argmax], axis=1) - - # Shape of next_target_values: (num_tau_prime_samples x batch_size) x 1. - target_quantile_values = tf.gather_nd( - self._replay_net_target_quantile_values, - batch_indexed_target_values)[:, None] - - return rewards + gamma_with_terminal * target_quantile_values - - def _build_train_op(self): - """Builds a training op. - - Returns: - train_op: An op performing one step of training from replay data. - """ - batch_size = tf.shape(self._replay.rewards)[0] - - target_quantile_values = tf.stop_gradient( - self._build_target_quantile_values_op()) - # Reshape to self.num_tau_prime_samples x batch_size x 1 since this is - # the manner in which the target_quantile_values are tiled. - target_quantile_values = tf.reshape(target_quantile_values, - [self.num_tau_prime_samples, - batch_size, 1]) - # Transpose dimensions so that the dimensionality is batch_size x - # self.num_tau_prime_samples x 1 to prepare for computation of - # Bellman errors. - # Final shape of target_quantile_values: - # batch_size x num_tau_prime_samples x 1. - target_quantile_values = tf.transpose(target_quantile_values, [1, 0, 2]) - - # Shape of indices: (num_tau_samples x batch_size) x 1. - # Expand dimension by one so that it can be used to index into all the - # quantiles when using the tf.gather_nd function (see below). - indices = tf.range(self.num_tau_samples * batch_size)[:, None] - - # Expand the dimension by one so that it can be used to index into all the - # quantiles when using the tf.gather_nd function (see below). - reshaped_actions = self._replay.actions[:, None] - reshaped_actions = tf.tile(reshaped_actions, [self.num_tau_samples, 1]) - # Shape of reshaped_actions: (num_tau_samples x batch_size) x 2. - reshaped_actions = tf.concat([indices, reshaped_actions], axis=1) - - chosen_action_quantile_values = tf.gather_nd( - self._replay_net_quantile_values, reshaped_actions) - print ('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>', self._replay_net_quantile_values) - # Transpose dimensions so that the dimensionality is batch_size x - # self.num_tau_samples x 1 to prepare for computation of - # Bellman errors. - # Reshape to self.num_tau_samples x batch_size x 1 since this is the manner - # in which the quantile values are tiled. - chosen_action_quantile_values = tf.reshape(chosen_action_quantile_values, - [self.num_tau_samples, - batch_size, 1]) - # Final shape of chosen_action_quantile_values: - # batch_size x num_tau_samples x 1. - chosen_action_quantile_values = tf.transpose( - chosen_action_quantile_values, [1, 0, 2]) #batchsize x quan x 1 - - ########################################################################################## - reshaped_actions1 = self._replay.actions[:, None] - reshaped_actions1 = tf.tile(reshaped_actions1, [self.num_tau_samples-1, 1]) - # Shape of reshaped_actions1: (num_tau_samples-1 x batch_size) x 2. - indices1 = tf.range((self.num_tau_samples-1) * batch_size)[:, None] - reshaped_actions1 = tf.concat([indices1, reshaped_actions1], axis=1) - gradient_tau = tf.reshape(self._replay_net_outputs.gradient_tau, (-1, self.num_actions)) #31 x 32 x 18 - print ('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>', gradient_tau) - gradient_tau = tf.gather_nd( - gradient_tau, reshaped_actions1) - print ('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>', gradient_tau) - chosen_action_gradient_tau = tf.reshape(gradient_tau, - [self.num_tau_samples-1, - batch_size, 1]) - self.chosen_action_gradient_tau = tf.transpose( - chosen_action_gradient_tau, [1, 0, 2]) #batchsize x quan x 1 (32 x 31 x 18) - self.chosen_action_gradient_tau = self.chosen_action_gradient_tau[:,:,0] #(32 x 31) - ########################################################################################## - - # Shape of bellman_erors and huber_loss: - # batch_size x num_tau_prime_samples x num_tau_samples x 1. - bellman_errors = target_quantile_values[:, :, None, :] - chosen_action_quantile_values[:, None, :, :] - #if 'fqf12' in self._runtype and 'fixbugtarg' in self._runtype: - # print ("============================================================= fixbug") - # print (bellman_errors.shape, self._replay_net_outputs.v_diff.shape, self.num_tau_samples) - # bellman_errors = bellman_errors * self._replay_net_outputs.v_diff[:,:,None,None] * self.num_tau_samples - # The huber loss (see Section 2.3 of the paper) is defined via two cases: - # case_one: |bellman_errors| <= kappa - # case_two: |bellman_errors| > kappa - huber_loss_case_one = tf.to_float( - tf.abs(bellman_errors) <= self.kappa) * 0.5 * bellman_errors ** 2 - huber_loss_case_two = tf.to_float( - tf.abs(bellman_errors) > self.kappa) * self.kappa * ( - tf.abs(bellman_errors) - 0.5 * self.kappa) - huber_loss = huber_loss_case_one + huber_loss_case_two - - # Reshape replay_quantiles to batch_size x num_tau_samples x 1 - replay_quantiles = tf.reshape( - self._replay_net_quantiles, [self.num_tau_samples, batch_size, 1]) - replay_quantiles = tf.transpose(replay_quantiles, [1, 0, 2]) #batchsize x quan x 1 - - # Tile by num_tau_prime_samples along a new dimension. Shape is now - # batch_size x num_tau_prime_samples x num_tau_samples x 1. - # These quantiles will be used for computation of the quantile huber loss - # below (see section 2.3 of the paper). - replay_quantiles = tf.to_float(tf.tile( - replay_quantiles[:, None, :, :], [1, self.num_tau_prime_samples, 1, 1])) - # Shape: batch_size x num_tau_prime_samples x num_tau_samples x 1. - quantile_huber_loss = (tf.abs(tf.stop_gradient(replay_quantiles) - tf.stop_gradient( - tf.to_float(bellman_errors < 0))) * huber_loss) / self.kappa - # Sum over current quantile value (num_tau_samples) dimension, - # average over target quantile value (num_tau_prime_samples) dimension. - # Shape: batch_size x num_tau_prime_samples x 1. - loss = tf.reduce_sum(quantile_huber_loss, axis=2) - # Shape: batch_size x 1. - loss = tf.reduce_mean(loss, axis=1) - - chosen_action_L_tau = tf.gather_nd(self._replay_net_outputs.L_tau, reshaped_actions) - print (">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>", chosen_action_L_tau.shape) - loss1 = tf.reduce_mean(chosen_action_L_tau, axis=0) - print (loss1.shape) - - update_priorities_op = tf.no_op() - with tf.control_dependencies([update_priorities_op]): - if self.summary_writer is not None: - with tf.variable_scope('Losses'): - tf.summary.scalar('QuantileLoss', tf.reduce_mean(loss)) - iqn_params, fqf_params = [], [] - params = tf.trainable_variables() - for p in params: - if 'fqf' in p.name and 'Target' not in p.name: fqf_params.append(p) - else: iqn_params.append(p) - print ("fqf_params:>>>>>>", fqf_params) - print ("iqn_params:>>>>>>", iqn_params) - #batchsize x quan - #batchsize x quan - #quan x batchsize - print ('================================================') - quantile_tau = tf.transpose(self._replay_net_outputs.quantile_tau, (1,0)) - q_entropy = tf.reduce_sum(-quantile_tau * tf.log(quantile_tau), axis=1) * 0.001 - #print (quantile_tau) #32x31 - print ("q_entropy:", q_entropy) - print (self.chosen_action_gradient_tau) #32x31 - print (fqf_params) - grads = tf.gradients(quantile_tau, fqf_params, grad_ys=self.chosen_action_gradient_tau) - print (grads) - grads_and_vars = [(grads[i], fqf_params[i]) for i in range(len(grads))] - return self.optimizer.minimize(tf.reduce_mean(loss), var_list=iqn_params), \ - self.optimizer1.apply_gradients(grads_and_vars), \ - self.optimizer1.minimize(self.ent * tf.reduce_mean(-q_entropy), var_list=fqf_params), \ - tf.reduce_mean(loss), tf.reduce_mean(loss1), \ - tf.squeeze(chosen_action_quantile_values), \ - tf.squeeze(replay_quantiles[:,0,:,:]), \ - self._replay_net_outputs.v_diff +# coding=utf-8 +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import numpy as np + + + +from dopamine.agents.rainbow import rainbow_agent +from dopamine.discrete_domains import atari_lib +import tensorflow as tf + +import gin.tf + +slim = tf.contrib.slim + + +@gin.configurable +class FQFAgent(rainbow_agent.RainbowAgent): + + def __init__(self, + sess, + num_actions, + network=atari_lib.fqf_network, + kappa=1.0, + runtype=None, + fqf_factor=0.000001, + fqf_ent=0.001, + num_tau_samples=32, + num_tau_prime_samples=32, + num_quantile_samples=32, + quantile_embedding_dim=64, + double_dqn=False, + summary_writer=None, + summary_writing_frequency=500): + """Initializes the agent and constructs the Graph. + + Most of this constructor's parameters are IQN-specific hyperparameters whose + values are taken from Dabney et al. (2018). + + Args: + sess: `tf.Session` object for running associated ops. + num_actions: int, number of actions the agent can take at any state. + network: function expecting three parameters: + (num_actions, network_type, state). This function will return the + network_type object containing the tensors output by the network. + See dopamine.discrete_domains.atari_lib.nature_dqn_network as + an example. + kappa: float, Huber loss cutoff. + num_tau_samples: int, number of online quantile samples for loss + estimation. + num_tau_prime_samples: int, number of target quantile samples for loss + estimation. + num_quantile_samples: int, number of quantile samples for computing + Q-values. + quantile_embedding_dim: int, embedding dimension for the quantile input. + double_dqn: boolean, whether to perform double DQN style learning + as described in Van Hasselt et al.: https://arxiv.org/abs/1509.06461. + summary_writer: SummaryWriter object for outputting training statistics. + Summary writing disabled if set to None. + summary_writing_frequency: int, frequency with which summaries will be + written. Lower values will result in slower training. + """ + print ('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>') + self._runtype= runtype + print (self._runtype) + self.fqf_factor = float(fqf_factor) + self.ent = float(fqf_ent) + self.kappa = kappa + print ('fqf factor:', self.fqf_factor) + # num_tau_samples = N below equation (3) in the paper. + self.num_tau_samples = num_tau_samples + # num_tau_prime_samples = N' below equation (3) in the paper. + self.num_tau_prime_samples = num_tau_prime_samples + # num_quantile_samples = k below equation (3) in the paper. + self.num_quantile_samples = num_quantile_samples + # quantile_embedding_dim = n above equation (4) in the paper. + self.quantile_embedding_dim = quantile_embedding_dim + # option to perform double dqn. + self.double_dqn = double_dqn + if 'adam' in self._runtype: + self.optimizer1 = tf.train.AdamOptimizer( + learning_rate=0.00005 * self.fqf_factor, + epsilon=0.0003125) + else: + self.optimizer1 = tf.train.RMSPropOptimizer( + learning_rate=0.00005 * self.fqf_factor, + decay=0.95, + momentum=0.0, + epsilon=0.00001, + centered=True) + + super(FQFAgent, self).__init__( + sess=sess, + num_actions=num_actions, + network=network, + summary_writer=summary_writer, + summary_writing_frequency=summary_writing_frequency) + + def _get_network_type(self): + return collections.namedtuple( + 'iqn_network', ['quantile_values', 'quantiles', 'quantile_values_origin', 'quantiles_origin', 'Fv_diff', 'v_diff', 'quantile_values_mid', 'quantiles_mid', 'L_tau', 'gradient_tau', 'quantile_tau']) + + def _network_template(self, state, num_quantiles): + return self.network(self.num_actions, self.quantile_embedding_dim, + self._get_network_type(), state, num_quantiles, self._runtype) + + def _train_step(self): + """Runs a single training step. + + Runs a training op if both: + (1) A minimum number of frames have been added to the replay buffer. + (2) `training_steps` is a multiple of `update_period`. + + Also, syncs weights from online to target network if training steps is a + multiple of target update period. + """ + # Run a train op at the rate of self.update_period if enough training steps + # have been run. This matches the Nature DQN behaviour. + if self._replay.memory.add_count > self.min_replay_history: + if self.training_steps % self.update_period == 0: + _, _, _, loss, loss1, quan_value, quan, vdiff = self._sess.run(self._train_op) + if self.training_steps % 50000 == 0: + batchsize = 32 + quan_value = np.reshape(quan_value, [batchsize, self.num_tau_samples]) + quan = np.reshape(quan, [batchsize, self.num_tau_samples]) + quan_value = quan_value[0].tolist() + quan = quan[0].tolist() + vdiff = vdiff[:, 0].tolist() + print (">>> loss:", loss) + print (">>> loss1:", loss1) + print (">>> value:", quan_value) + print (">>> quans:", quan) + print (">>> vdiff:", vdiff) + print (">>> vdiff_sum:", np.sum(vdiff)) + if (self.summary_writer is not None and + self.training_steps > 0 and + self.training_steps % self.summary_writing_frequency == 0): + summary = self._sess.run(self._merged_summaries) + self.summary_writer.add_summary(summary, self.training_steps) + + if self.training_steps % self.target_update_period == 0: + self._sess.run(self._sync_qt_ops) + + self.training_steps += 1 + + def _build_networks(self): + """Builds the FQF computations needed for acting and training. + + These are: + self.online_convnet: For computing the current state's quantile values. + self.target_convnet: For computing the next state's target quantile + values. + self._net_outputs: The actual quantile values. + self._q_argmax: The action maximizing the current state's Q-values. + self._replay_net_outputs: The replayed states' quantile values. + self._replay_next_target_net_outputs: The replayed next states' target + quantile values. + """ + # Calling online_convnet will generate a new graph as defined in + # self._get_network_template using whatever input is passed, but will always + # share the same weights. + self.online_convnet = tf.make_template('Online', self._network_template) + self.target_convnet = tf.make_template('Target', self._network_template) + + # Compute the Q-values which are used for action selection in the current + # state. + self._net_outputs = self.online_convnet(self.state_ph, + self.num_quantile_samples) + # Shape of self._net_outputs.quantile_values: + # num_quantile_samples x num_actions. + # e.g. if num_actions is 2, it might look something like this: + # Vals for Quantile .2 Vals for Quantile .4 Vals for Quantile .6 + # [[0.1, 0.5], [0.15, -0.3], [0.15, -0.2]] + # Q-values = [(0.1 + 0.15 + 0.15)/3, (0.5 + 0.15 + -0.2)/3]. + if 'ws' in self._runtype: + self._q_values = tf.reduce_sum(self._net_outputs.quantile_values * self._net_outputs.v_diff, axis=0) #NOTE: quantile_values = quantile_values_mid + else: + self._q_values = tf.reduce_mean(self._net_outputs.quantile_values, axis=0) + self._q_argmax = tf.argmax(self._q_values, axis=0) + + self._replay_net_outputs = self.online_convnet(self._replay.states, + self.num_tau_samples) + # Shape: (num_tau_samples x batch_size) x num_actions. + self._replay_net_quantile_values = self._replay_net_outputs.quantile_values + self._replay_net_quantiles = self._replay_net_outputs.quantiles + + # Do the same for next states in the replay buffer. + self._replay_net_target_outputs = self.target_convnet( + self._replay.next_states, self.num_tau_prime_samples) + # Shape: (num_tau_prime_samples x batch_size) x num_actions. + vals = self._replay_net_target_outputs.quantile_values + self._replay_net_target_quantile_values = vals + + # Compute Q-values which are used for action selection for the next states + # in the replay buffer. Compute the argmax over the Q-values. + if self.double_dqn: + outputs_action = self.online_convnet(self._replay.next_states, + self.num_quantile_samples) + else: + outputs_action = self.target_convnet(self._replay.next_states, + self.num_quantile_samples) + + # Shape: (num_quantile_samples x batch_size) x num_actions. + target_quantile_values_action = outputs_action.quantile_values #NOTE: quantile_values = quantile_values_mid + # Shape: num_quantile_samples x batch_size x num_actions. + target_quantile_values_action = tf.reshape(target_quantile_values_action, + [self.num_quantile_samples, + self._replay.batch_size, + self.num_actions]) + # Shape: batch_size x num_actions. + if 'ws' in self._runtype: + v_diff = tf.reshape(outputs_action.v_diff, [self.num_quantile_samples, self._replay.batch_size, 1]) + self._replay_net_target_q_values = tf.squeeze(tf.reduce_sum( + target_quantile_values_action * v_diff, axis=0)) + else: + self._replay_net_target_q_values = tf.squeeze(tf.reduce_mean( + target_quantile_values_action, axis=0)) + self._replay_next_qt_argmax = tf.argmax( + self._replay_net_target_q_values, axis=1) + + def _build_target_quantile_values_op(self): + """Build an op used as a target for return values at given quantiles. + + Returns: + An op calculating the target quantile return. + """ + batch_size = tf.shape(self._replay.rewards)[0] + # Shape of rewards: (num_tau_prime_samples x batch_size) x 1. + rewards = self._replay.rewards[:, None] + rewards = tf.tile(rewards, [self.num_tau_prime_samples, 1]) + + is_terminal_multiplier = 1. - tf.to_float(self._replay.terminals) + # Incorporate terminal state to discount factor. + # size of gamma_with_terminal: (num_tau_prime_samples x batch_size) x 1. + gamma_with_terminal = self.cumulative_gamma * is_terminal_multiplier + gamma_with_terminal = tf.tile(gamma_with_terminal[:, None], + [self.num_tau_prime_samples, 1]) + + # Get the indices of the maximium Q-value across the action dimension. + # Shape of replay_next_qt_argmax: (num_tau_prime_samples x batch_size) x 1. + + replay_next_qt_argmax = tf.tile( + self._replay_next_qt_argmax[:, None], [self.num_tau_prime_samples, 1]) + + # Shape of batch_indices: (num_tau_prime_samples x batch_size) x 1. + batch_indices = tf.cast(tf.range( + self.num_tau_prime_samples * batch_size)[:, None], tf.int64) + + # Shape of batch_indexed_target_values: + # (num_tau_prime_samples x batch_size) x 2. + batch_indexed_target_values = tf.concat( + [batch_indices, replay_next_qt_argmax], axis=1) + + # Shape of next_target_values: (num_tau_prime_samples x batch_size) x 1. + target_quantile_values = tf.gather_nd( + self._replay_net_target_quantile_values, + batch_indexed_target_values)[:, None] + + return rewards + gamma_with_terminal * target_quantile_values + + def _build_train_op(self): + """Builds a training op. + + Returns: + train_op: An op performing one step of training from replay data. + """ + batch_size = tf.shape(self._replay.rewards)[0] + + target_quantile_values = tf.stop_gradient( + self._build_target_quantile_values_op()) + # Reshape to self.num_tau_prime_samples x batch_size x 1 since this is + # the manner in which the target_quantile_values are tiled. + target_quantile_values = tf.reshape(target_quantile_values, + [self.num_tau_prime_samples, + batch_size, 1]) + # Transpose dimensions so that the dimensionality is batch_size x + # self.num_tau_prime_samples x 1 to prepare for computation of + # Bellman errors. + # Final shape of target_quantile_values: + # batch_size x num_tau_prime_samples x 1. + target_quantile_values = tf.transpose(target_quantile_values, [1, 0, 2]) + + # Shape of indices: (num_tau_samples x batch_size) x 1. + # Expand dimension by one so that it can be used to index into all the + # quantiles when using the tf.gather_nd function (see below). + indices = tf.range(self.num_tau_samples * batch_size)[:, None] + + # Expand the dimension by one so that it can be used to index into all the + # quantiles when using the tf.gather_nd function (see below). + reshaped_actions = self._replay.actions[:, None] + reshaped_actions = tf.tile(reshaped_actions, [self.num_tau_samples, 1]) + # Shape of reshaped_actions: (num_tau_samples x batch_size) x 2. + reshaped_actions = tf.concat([indices, reshaped_actions], axis=1) + + chosen_action_quantile_values = tf.gather_nd( + self._replay_net_quantile_values, reshaped_actions) + print ('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>', self._replay_net_quantile_values) + # Transpose dimensions so that the dimensionality is batch_size x + # self.num_tau_samples x 1 to prepare for computation of + # Bellman errors. + # Reshape to self.num_tau_samples x batch_size x 1 since this is the manner + # in which the quantile values are tiled. + chosen_action_quantile_values = tf.reshape(chosen_action_quantile_values, + [self.num_tau_samples, + batch_size, 1]) + # Final shape of chosen_action_quantile_values: + # batch_size x num_tau_samples x 1. + chosen_action_quantile_values = tf.transpose( + chosen_action_quantile_values, [1, 0, 2]) #batchsize x quan x 1 + + ########################################################################################## + reshaped_actions1 = self._replay.actions[:, None] + reshaped_actions1 = tf.tile(reshaped_actions1, [self.num_tau_samples-1, 1]) + # Shape of reshaped_actions1: (num_tau_samples-1 x batch_size) x 2. + indices1 = tf.range((self.num_tau_samples-1) * batch_size)[:, None] + reshaped_actions1 = tf.concat([indices1, reshaped_actions1], axis=1) + gradient_tau = tf.reshape(self._replay_net_outputs.gradient_tau, (-1, self.num_actions)) #31 x 32 x 18 + print ('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>', gradient_tau) + gradient_tau = tf.gather_nd( + gradient_tau, reshaped_actions1) + print ('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>', gradient_tau) + chosen_action_gradient_tau = tf.reshape(gradient_tau, + [self.num_tau_samples-1, + batch_size, 1]) + self.chosen_action_gradient_tau = tf.transpose( + chosen_action_gradient_tau, [1, 0, 2]) #batchsize x quan x 1 (32 x 31 x 18) + self.chosen_action_gradient_tau = self.chosen_action_gradient_tau[:,:,0] #(32 x 31) + ########################################################################################## + + # Shape of bellman_erors and huber_loss: + # batch_size x num_tau_prime_samples x num_tau_samples x 1. + bellman_errors = target_quantile_values[:, :, None, :] - chosen_action_quantile_values[:, None, :, :] + #if 'fqf12' in self._runtype and 'fixbugtarg' in self._runtype: + # print ("============================================================= fixbug") + # print (bellman_errors.shape, self._replay_net_outputs.v_diff.shape, self.num_tau_samples) + # bellman_errors = bellman_errors * self._replay_net_outputs.v_diff[:,:,None,None] * self.num_tau_samples + # The huber loss (see Section 2.3 of the paper) is defined via two cases: + # case_one: |bellman_errors| <= kappa + # case_two: |bellman_errors| > kappa + huber_loss_case_one = tf.to_float( + tf.abs(bellman_errors) <= self.kappa) * 0.5 * bellman_errors ** 2 + huber_loss_case_two = tf.to_float( + tf.abs(bellman_errors) > self.kappa) * self.kappa * ( + tf.abs(bellman_errors) - 0.5 * self.kappa) + huber_loss = huber_loss_case_one + huber_loss_case_two + + # Reshape replay_quantiles to batch_size x num_tau_samples x 1 + replay_quantiles = tf.reshape( + self._replay_net_quantiles, [self.num_tau_samples, batch_size, 1]) + replay_quantiles = tf.transpose(replay_quantiles, [1, 0, 2]) #batchsize x quan x 1 + + # Tile by num_tau_prime_samples along a new dimension. Shape is now + # batch_size x num_tau_prime_samples x num_tau_samples x 1. + # These quantiles will be used for computation of the quantile huber loss + # below (see section 2.3 of the paper). + replay_quantiles = tf.to_float(tf.tile( + replay_quantiles[:, None, :, :], [1, self.num_tau_prime_samples, 1, 1])) + # Shape: batch_size x num_tau_prime_samples x num_tau_samples x 1. + quantile_huber_loss = (tf.abs(tf.stop_gradient(replay_quantiles) - tf.stop_gradient( + tf.to_float(bellman_errors < 0))) * huber_loss) / self.kappa + # Sum over current quantile value (num_tau_samples) dimension, + # average over target quantile value (num_tau_prime_samples) dimension. + # Shape: batch_size x num_tau_prime_samples x 1. + loss = tf.reduce_sum(quantile_huber_loss, axis=2) + # Shape: batch_size x 1. + loss = tf.reduce_mean(loss, axis=1) + + chosen_action_L_tau = tf.gather_nd(self._replay_net_outputs.L_tau, reshaped_actions) + print (">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>", chosen_action_L_tau.shape) + loss1 = tf.reduce_mean(chosen_action_L_tau, axis=0) + print (loss1.shape) + + update_priorities_op = tf.no_op() + with tf.control_dependencies([update_priorities_op]): + if self.summary_writer is not None: + with tf.variable_scope('Losses'): + tf.summary.scalar('QuantileLoss', tf.reduce_mean(loss)) + iqn_params, fqf_params = [], [] + params = tf.trainable_variables() + for p in params: + if 'fqf' in p.name and 'Target' not in p.name: fqf_params.append(p) + else: iqn_params.append(p) + print ("fqf_params:>>>>>>", fqf_params) + print ("iqn_params:>>>>>>", iqn_params) + #batchsize x quan + #batchsize x quan + #quan x batchsize + print ('================================================') + quantile_tau = tf.transpose(self._replay_net_outputs.quantile_tau, (1,0)) + q_entropy = tf.reduce_sum(-quantile_tau * tf.log(quantile_tau), axis=1) * 0.001 + #print (quantile_tau) #32x31 + print ("q_entropy:", q_entropy) + print (self.chosen_action_gradient_tau) #32x31 + print (fqf_params) + grads = tf.gradients(quantile_tau, fqf_params, grad_ys=self.chosen_action_gradient_tau) + print (grads) + grads_and_vars = [(grads[i], fqf_params[i]) for i in range(len(grads))] + if 'sqloss' in self._runtype: + print ('use sqloss') + return self.optimizer.minimize(tf.reduce_mean(loss), var_list=iqn_params), \ + self.optimizer1.minimize(tf.reduce_mean(loss1), var_list=fqf_params), \ + tf.reduce_mean(loss), tf.reduce_mean(loss1), \ + tf.squeeze(chosen_action_quantile_values), \ + tf.squeeze(replay_quantiles[:,0,:,:]), \ + self._replay_net_outputs.v_diff + else: + print ('use directBP') + return self.optimizer.minimize(tf.reduce_mean(loss), var_list=iqn_params), \ + self.optimizer1.apply_gradients(grads_and_vars), \ + self.optimizer1.minimize(self.ent * tf.reduce_mean(-q_entropy), var_list=fqf_params), \ + tf.reduce_mean(loss), tf.reduce_mean(loss1), \ + tf.squeeze(chosen_action_quantile_values), \ + tf.squeeze(replay_quantiles[:,0,:,:]), \ + self._replay_net_outputs.v_diff diff --git a/dopamine/agents/implicit_quantile/__init__.py b/dopamine/agents/implicit_quantile/__init__.py index 920cbb5..dc108ba 100644 --- a/dopamine/agents/implicit_quantile/__init__.py +++ b/dopamine/agents/implicit_quantile/__init__.py @@ -1,15 +1,15 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/dopamine/agents/implicit_quantile/configs/implicit_quantile.gin b/dopamine/agents/implicit_quantile/configs/implicit_quantile.gin index a1f20e9..e657ccd 100644 --- a/dopamine/agents/implicit_quantile/configs/implicit_quantile.gin +++ b/dopamine/agents/implicit_quantile/configs/implicit_quantile.gin @@ -1,46 +1,46 @@ -# Hyperparameters follow Dabney et al. (2018), but we modify as necessary to -# match those used in Rainbow (Hessel et al., 2018), to ensure apples-to-apples -# comparison. -import dopamine.agents.implicit_quantile.implicit_quantile_agent -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -ImplicitQuantileAgent.kappa = 1.0 -ImplicitQuantileAgent.num_tau_samples = 32 -ImplicitQuantileAgent.num_tau_prime_samples = 32 -ImplicitQuantileAgent.num_quantile_samples = 32 -ImplicitQuantileAgent.runtype = 'RUNTYPE' -RainbowAgent.gamma = 0.99 -RainbowAgent.game = 'GAME' -RainbowAgent.runtype = 'RUNTYPE' -RainbowAgent.update_horizon = 1 -RainbowAgent.min_replay_history = 20000 # agent steps -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 8000 # agent steps -RainbowAgent.epsilon_train = 0.01 -RainbowAgent.epsilon_eval = 0.001 -RainbowAgent.epsilon_decay_period = 250000 # agent steps -# IQN currently does not support prioritized replay. -RainbowAgent.replay_scheme = 'uniform' -RainbowAgent.tf_device = '/gpu:0' # '/cpu:*' use for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.00005 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -atari_lib.create_atari_environment.game_name = 'GAME' -# Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). -atari_lib.create_atari_environment.sticky_actions = True -create_agent.agent_name = 'implicit_quantile' -Runner.num_iterations = 200 -Runner.game = 'GAME' -Runner.runtype = 'RUNTYPE' -Runner.training_steps = 250000 -Runner.evaluation_steps = 125000 -Runner.max_steps_per_episode = 27000 - -WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 -WrappedPrioritizedReplayBuffer.batch_size = 32 +# Hyperparameters follow Dabney et al. (2018), but we modify as necessary to +# match those used in Rainbow (Hessel et al., 2018), to ensure apples-to-apples +# comparison. +import dopamine.agents.implicit_quantile.implicit_quantile_agent +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +ImplicitQuantileAgent.kappa = 1.0 +ImplicitQuantileAgent.num_tau_samples = 32 +ImplicitQuantileAgent.num_tau_prime_samples = 32 +ImplicitQuantileAgent.num_quantile_samples = 32 +ImplicitQuantileAgent.runtype = 'RUNTYPE' +RainbowAgent.gamma = 0.99 +RainbowAgent.game = 'GAME' +RainbowAgent.runtype = 'RUNTYPE' +RainbowAgent.update_horizon = 1 +RainbowAgent.min_replay_history = 20000 # agent steps +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 8000 # agent steps +RainbowAgent.epsilon_train = 0.01 +RainbowAgent.epsilon_eval = 0.001 +RainbowAgent.epsilon_decay_period = 250000 # agent steps +# IQN currently does not support prioritized replay. +RainbowAgent.replay_scheme = 'uniform' +RainbowAgent.tf_device = '/gpu:0' # '/cpu:*' use for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.00005 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +atari_lib.create_atari_environment.game_name = 'GAME' +# Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). +atari_lib.create_atari_environment.sticky_actions = True +create_agent.agent_name = 'implicit_quantile' +Runner.num_iterations = 200 +Runner.game = 'GAME' +Runner.runtype = 'RUNTYPE' +Runner.training_steps = 250000 +Runner.evaluation_steps = 125000 +Runner.max_steps_per_episode = 27000 + +WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 +WrappedPrioritizedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin b/dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin index 9c4514e..7a8dcec 100644 --- a/dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin +++ b/dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin @@ -1,43 +1,43 @@ -# Hyperparameters follow Dabney et al. (2018). -import dopamine.agents.implicit_quantile.implicit_quantile_agent -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -ImplicitQuantileAgent.kappa = 1.0 -ImplicitQuantileAgent.num_tau_samples = 32 -ImplicitQuantileAgent.num_tau_prime_samples = 32 -ImplicitQuantileAgent.num_quantile_samples = 32 -RainbowAgent.gamma = 0.99 -RainbowAgent.game = 'GAME' -RainbowAgent.runtype = 'RUNTYPE' -RainbowAgent.update_horizon = 1 -RainbowAgent.min_replay_history = 50000 # agent steps -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 10000 # agent steps -RainbowAgent.epsilon_train = 0.01 -RainbowAgent.epsilon_eval = 0.001 -RainbowAgent.epsilon_decay_period = 1000000 # agent steps -RainbowAgent.replay_scheme = 'uniform' -RainbowAgent.tf_device = '/gpu:0' # '/cpu:*' use for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.00005 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -atari_lib.create_atari_environment.game_name = 'GAME' -atari_lib.create_atari_environment.sticky_actions = False -create_agent.agent_name = 'implicit_quantile' -Runner.num_iterations = 200 -Runner.game = 'GAME' -Runner.runtype = 'RUNTYPE' -Runner.training_steps = 250000 -Runner.evaluation_steps = 125000 -Runner.max_steps_per_episode = 27000 - -AtariPreprocessing.terminal_on_life_loss = True - -WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 -WrappedPrioritizedReplayBuffer.batch_size = 32 +# Hyperparameters follow Dabney et al. (2018). +import dopamine.agents.implicit_quantile.implicit_quantile_agent +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +ImplicitQuantileAgent.kappa = 1.0 +ImplicitQuantileAgent.num_tau_samples = 32 +ImplicitQuantileAgent.num_tau_prime_samples = 32 +ImplicitQuantileAgent.num_quantile_samples = 32 +RainbowAgent.gamma = 0.99 +RainbowAgent.game = 'GAME' +RainbowAgent.runtype = 'RUNTYPE' +RainbowAgent.update_horizon = 1 +RainbowAgent.min_replay_history = 50000 # agent steps +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 10000 # agent steps +RainbowAgent.epsilon_train = 0.01 +RainbowAgent.epsilon_eval = 0.001 +RainbowAgent.epsilon_decay_period = 1000000 # agent steps +RainbowAgent.replay_scheme = 'uniform' +RainbowAgent.tf_device = '/gpu:0' # '/cpu:*' use for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.00005 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +atari_lib.create_atari_environment.game_name = 'GAME' +atari_lib.create_atari_environment.sticky_actions = False +create_agent.agent_name = 'implicit_quantile' +Runner.num_iterations = 200 +Runner.game = 'GAME' +Runner.runtype = 'RUNTYPE' +Runner.training_steps = 250000 +Runner.evaluation_steps = 125000 +Runner.max_steps_per_episode = 27000 + +AtariPreprocessing.terminal_on_life_loss = True + +WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 +WrappedPrioritizedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/implicit_quantile/implicit_quantile_agent.py b/dopamine/agents/implicit_quantile/implicit_quantile_agent.py index 178db23..ba7470f 100644 --- a/dopamine/agents/implicit_quantile/implicit_quantile_agent.py +++ b/dopamine/agents/implicit_quantile/implicit_quantile_agent.py @@ -1,348 +1,348 @@ -#1 coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""The implicit quantile networks (IQN) agent. - -The agent follows the description given in "Implicit Quantile Networks for -Distributional RL" (Dabney et. al, 2018). -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import numpy as np - - - -from dopamine.agents.rainbow import rainbow_agent -from dopamine.discrete_domains import atari_lib -import tensorflow as tf - -import gin.tf - -slim = tf.contrib.slim - - -@gin.configurable -class ImplicitQuantileAgent(rainbow_agent.RainbowAgent): - """An extension of Rainbow to perform implicit quantile regression.""" - - def __init__(self, - sess, - num_actions, - network=atari_lib.implicit_quantile_network, - kappa=1.0, - num_tau_samples=32, - num_tau_prime_samples=32, - num_quantile_samples=32, - quantile_embedding_dim=64, - double_dqn=False, - summary_writer=None, - summary_writing_frequency=500): - """Initializes the agent and constructs the Graph. - - Most of this constructor's parameters are IQN-specific hyperparameters whose - values are taken from Dabney et al. (2018). - - Args: - sess: `tf.Session` object for running associated ops. - num_actions: int, number of actions the agent can take at any state. - network: function expecting three parameters: - (num_actions, network_type, state). This function will return the - network_type object containing the tensors output by the network. - See dopamine.discrete_domains.atari_lib.nature_dqn_network as - an example. - kappa: float, Huber loss cutoff. - num_tau_samples: int, number of online quantile samples for loss - estimation. - num_tau_prime_samples: int, number of target quantile samples for loss - estimation. - num_quantile_samples: int, number of quantile samples for computing - Q-values. - quantile_embedding_dim: int, embedding dimension for the quantile input. - double_dqn: boolean, whether to perform double DQN style learning - as described in Van Hasselt et al.: https://arxiv.org/abs/1509.06461. - summary_writer: SummaryWriter object for outputting training statistics. - Summary writing disabled if set to None. - summary_writing_frequency: int, frequency with which summaries will be - written. Lower values will result in slower training. - """ - self.kappa = kappa - # num_tau_samples = N below equation (3) in the paper. - self.num_tau_samples = num_tau_samples - # num_tau_prime_samples = N' below equation (3) in the paper. - self.num_tau_prime_samples = num_tau_prime_samples - # num_quantile_samples = k below equation (3) in the paper. - self.num_quantile_samples = num_quantile_samples - # quantile_embedding_dim = n above equation (4) in the paper. - self.quantile_embedding_dim = quantile_embedding_dim - # option to perform double dqn. - self.double_dqn = double_dqn - - super(ImplicitQuantileAgent, self).__init__( - sess=sess, - num_actions=num_actions, - network=network, - summary_writer=summary_writer, - summary_writing_frequency=summary_writing_frequency) - - def _get_network_type(self): - """Returns the type of the outputs of the implicit quantile network. - - Returns: - _network_type object defining the outputs of the network. - """ - return collections.namedtuple( - 'iqn_network', ['quantile_values', 'quantiles']) - - def _network_template(self, state, num_quantiles): - r"""Builds an Implicit Quantile ConvNet. - - Takes state and quantile as inputs and outputs state-action quantile values. - - Args: - state: A `tf.placeholder` for the RL state. - num_quantiles: int, number of quantile inputs. - - Returns: - _network_type object containing quantile value outputs of the network. - """ - return self.network(self.num_actions, self.quantile_embedding_dim, - self._get_network_type(), state, num_quantiles) - - def _train_step(self): - """Runs a single training step. - - Runs a training op if both: - (1) A minimum number of frames have been added to the replay buffer. - (2) `training_steps` is a multiple of `update_period`. - - Also, syncs weights from online to target network if training steps is a - multiple of target update period. - """ - # Run a train op at the rate of self.update_period if enough training steps - # have been run. This matches the Nature DQN behaviour. - if self._replay.memory.add_count > self.min_replay_history: - if self.training_steps % self.update_period == 0: - self._sess.run(self._train_op) - if (self.summary_writer is not None and - self.training_steps > 0 and - self.training_steps % self.summary_writing_frequency == 0): - summary = self._sess.run(self._merged_summaries) - self.summary_writer.add_summary(summary, self.training_steps) - - if self.training_steps % self.target_update_period == 0: - self._sess.run(self._sync_qt_ops) - - self.training_steps += 1 - - def _build_networks(self): - """Builds the IQN computations needed for acting and training. - - These are: - self.online_convnet: For computing the current state's quantile values. - self.target_convnet: For computing the next state's target quantile - values. - self._net_outputs: The actual quantile values. - self._q_argmax: The action maximizing the current state's Q-values. - self._replay_net_outputs: The replayed states' quantile values. - self._replay_next_target_net_outputs: The replayed next states' target - quantile values. - """ - # Calling online_convnet will generate a new graph as defined in - # self._get_network_template using whatever input is passed, but will always - # share the same weights. - self.online_convnet = tf.make_template('Online', self._network_template) - self.target_convnet = tf.make_template('Target', self._network_template) - - # Compute the Q-values which are used for action selection in the current - # state. - self._net_outputs = self.online_convnet(self.state_ph, - self.num_quantile_samples) - # Shape of self._net_outputs.quantile_values: - # num_quantile_samples x num_actions. - # e.g. if num_actions is 2, it might look something like this: - # Vals for Quantile .2 Vals for Quantile .4 Vals for Quantile .6 - # [[0.1, 0.5], [0.15, -0.3], [0.15, -0.2]] - # Q-values = [(0.1 + 0.15 + 0.15)/3, (0.5 + 0.15 + -0.2)/3]. - self._q_values = tf.reduce_mean(self._net_outputs.quantile_values, axis=0) - self._q_argmax = tf.argmax(self._q_values, axis=0) - - self._replay_net_outputs = self.online_convnet(self._replay.states, - self.num_tau_samples) - # Shape: (num_tau_samples x batch_size) x num_actions. - self._replay_net_quantile_values = self._replay_net_outputs.quantile_values - self._replay_net_quantiles = self._replay_net_outputs.quantiles - - # Do the same for next states in the replay buffer. - self._replay_net_target_outputs = self.target_convnet( - self._replay.next_states, self.num_tau_prime_samples) - # Shape: (num_tau_prime_samples x batch_size) x num_actions. - vals = self._replay_net_target_outputs.quantile_values - self._replay_net_target_quantile_values = vals - - # Compute Q-values which are used for action selection for the next states - # in the replay buffer. Compute the argmax over the Q-values. - if self.double_dqn: - outputs_action = self.online_convnet(self._replay.next_states, - self.num_quantile_samples) - else: - outputs_action = self.target_convnet(self._replay.next_states, - self.num_quantile_samples) - - # Shape: (num_quantile_samples x batch_size) x num_actions. - target_quantile_values_action = outputs_action.quantile_values - # Shape: num_quantile_samples x batch_size x num_actions. - target_quantile_values_action = tf.reshape(target_quantile_values_action, - [self.num_quantile_samples, - self._replay.batch_size, - self.num_actions]) - # Shape: batch_size x num_actions. - self._replay_net_target_q_values = tf.squeeze(tf.reduce_mean( - target_quantile_values_action, axis=0)) - self._replay_next_qt_argmax = tf.argmax( - self._replay_net_target_q_values, axis=1) - - def _build_target_quantile_values_op(self): - """Build an op used as a target for return values at given quantiles. - - Returns: - An op calculating the target quantile return. - """ - batch_size = tf.shape(self._replay.rewards)[0] - # Shape of rewards: (num_tau_prime_samples x batch_size) x 1. - rewards = self._replay.rewards[:, None] - rewards = tf.tile(rewards, [self.num_tau_prime_samples, 1]) - - is_terminal_multiplier = 1. - tf.to_float(self._replay.terminals) - # Incorporate terminal state to discount factor. - # size of gamma_with_terminal: (num_tau_prime_samples x batch_size) x 1. - gamma_with_terminal = self.cumulative_gamma * is_terminal_multiplier - gamma_with_terminal = tf.tile(gamma_with_terminal[:, None], - [self.num_tau_prime_samples, 1]) - - # Get the indices of the maximium Q-value across the action dimension. - # Shape of replay_next_qt_argmax: (num_tau_prime_samples x batch_size) x 1. - - replay_next_qt_argmax = tf.tile( - self._replay_next_qt_argmax[:, None], [self.num_tau_prime_samples, 1]) - - # Shape of batch_indices: (num_tau_prime_samples x batch_size) x 1. - batch_indices = tf.cast(tf.range( - self.num_tau_prime_samples * batch_size)[:, None], tf.int64) - - # Shape of batch_indexed_target_values: - # (num_tau_prime_samples x batch_size) x 2. - batch_indexed_target_values = tf.concat( - [batch_indices, replay_next_qt_argmax], axis=1) - - # Shape of next_target_values: (num_tau_prime_samples x batch_size) x 1. - target_quantile_values = tf.gather_nd( - self._replay_net_target_quantile_values, - batch_indexed_target_values)[:, None] - - return rewards + gamma_with_terminal * target_quantile_values - - def _build_train_op(self): - """Builds a training op. - - Returns: - train_op: An op performing one step of training from replay data. - """ - batch_size = tf.shape(self._replay.rewards)[0] - - target_quantile_values = tf.stop_gradient( - self._build_target_quantile_values_op()) - # Reshape to self.num_tau_prime_samples x batch_size x 1 since this is - # the manner in which the target_quantile_values are tiled. - target_quantile_values = tf.reshape(target_quantile_values, - [self.num_tau_prime_samples, - batch_size, 1]) - # Transpose dimensions so that the dimensionality is batch_size x - # self.num_tau_prime_samples x 1 to prepare for computation of - # Bellman errors. - # Final shape of target_quantile_values: - # batch_size x num_tau_prime_samples x 1. - target_quantile_values = tf.transpose(target_quantile_values, [1, 0, 2]) - - # Shape of indices: (num_tau_samples x batch_size) x 1. - # Expand dimension by one so that it can be used to index into all the - # quantiles when using the tf.gather_nd function (see below). - indices = tf.range(self.num_tau_samples * batch_size)[:, None] - - # Expand the dimension by one so that it can be used to index into all the - # quantiles when using the tf.gather_nd function (see below). - reshaped_actions = self._replay.actions[:, None] - reshaped_actions = tf.tile(reshaped_actions, [self.num_tau_samples, 1]) - # Shape of reshaped_actions: (num_tau_samples x batch_size) x 2. - reshaped_actions = tf.concat([indices, reshaped_actions], axis=1) - - chosen_action_quantile_values = tf.gather_nd( - self._replay_net_quantile_values, reshaped_actions) - # Transpose dimensions so that the dimensionality is batch_size x - # self.num_tau_samples x 1 to prepare for computation of - # Bellman errors. - # Reshape to self.num_tau_samples x batch_size x 1 since this is the manner - # in which the quantile values are tiled. - chosen_action_quantile_values = tf.reshape(chosen_action_quantile_values, - [self.num_tau_samples, - batch_size, 1]) - # Final shape of chosen_action_quantile_values: - # batch_size x num_tau_samples x 1. - chosen_action_quantile_values = tf.transpose( - chosen_action_quantile_values, [1, 0, 2]) #batchsize x quan x 1 - - # Shape of bellman_erors and huber_loss: - # batch_size x num_tau_prime_samples x num_tau_samples x 1. - bellman_errors = target_quantile_values[:, :, None, :] - chosen_action_quantile_values[:, None, :, :] - # The huber loss (see Section 2.3 of the paper) is defined via two cases: - # case_one: |bellman_errors| <= kappa - # case_two: |bellman_errors| > kappa - huber_loss_case_one = tf.to_float( - tf.abs(bellman_errors) <= self.kappa) * 0.5 * bellman_errors ** 2 - huber_loss_case_two = tf.to_float( - tf.abs(bellman_errors) > self.kappa) * self.kappa * ( - tf.abs(bellman_errors) - 0.5 * self.kappa) - huber_loss = huber_loss_case_one + huber_loss_case_two - - # Reshape replay_quantiles to batch_size x num_tau_samples x 1 - replay_quantiles = tf.reshape( - self._replay_net_quantiles, [self.num_tau_samples, batch_size, 1]) - replay_quantiles = tf.transpose(replay_quantiles, [1, 0, 2]) #batchsize x quan x 1 - - # Tile by num_tau_prime_samples along a new dimension. Shape is now - # batch_size x num_tau_prime_samples x num_tau_samples x 1. - # These quantiles will be used for computation of the quantile huber loss - # below (see section 2.3 of the paper). - replay_quantiles = tf.to_float(tf.tile( - replay_quantiles[:, None, :, :], [1, self.num_tau_prime_samples, 1, 1])) - # Shape: batch_size x num_tau_prime_samples x num_tau_samples x 1. - quantile_huber_loss = (tf.abs(tf.stop_gradient(replay_quantiles) - tf.stop_gradient( - tf.to_float(bellman_errors < 0))) * huber_loss) / self.kappa - # Sum over current quantile value (num_tau_samples) dimension, - # average over target quantile value (num_tau_prime_samples) dimension. - # Shape: batch_size x num_tau_prime_samples x 1. - loss = tf.reduce_sum(quantile_huber_loss, axis=2) - # Shape: batch_size x 1. - loss = tf.reduce_mean(loss, axis=1) - - # TODO(kumasaurabh): Add prioritized replay functionality here. - update_priorities_op = tf.no_op() - with tf.control_dependencies([update_priorities_op]): - if self.summary_writer is not None: - with tf.variable_scope('Losses'): - tf.summary.scalar('QuantileLoss', tf.reduce_mean(loss)) - return self.optimizer.minimize(tf.reduce_mean(loss)) +#1 coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""The implicit quantile networks (IQN) agent. + +The agent follows the description given in "Implicit Quantile Networks for +Distributional RL" (Dabney et. al, 2018). +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import numpy as np + + + +from dopamine.agents.rainbow import rainbow_agent +from dopamine.discrete_domains import atari_lib +import tensorflow as tf + +import gin.tf + +slim = tf.contrib.slim + + +@gin.configurable +class ImplicitQuantileAgent(rainbow_agent.RainbowAgent): + """An extension of Rainbow to perform implicit quantile regression.""" + + def __init__(self, + sess, + num_actions, + network=atari_lib.implicit_quantile_network, + kappa=1.0, + num_tau_samples=32, + num_tau_prime_samples=32, + num_quantile_samples=32, + quantile_embedding_dim=64, + double_dqn=False, + summary_writer=None, + summary_writing_frequency=500): + """Initializes the agent and constructs the Graph. + + Most of this constructor's parameters are IQN-specific hyperparameters whose + values are taken from Dabney et al. (2018). + + Args: + sess: `tf.Session` object for running associated ops. + num_actions: int, number of actions the agent can take at any state. + network: function expecting three parameters: + (num_actions, network_type, state). This function will return the + network_type object containing the tensors output by the network. + See dopamine.discrete_domains.atari_lib.nature_dqn_network as + an example. + kappa: float, Huber loss cutoff. + num_tau_samples: int, number of online quantile samples for loss + estimation. + num_tau_prime_samples: int, number of target quantile samples for loss + estimation. + num_quantile_samples: int, number of quantile samples for computing + Q-values. + quantile_embedding_dim: int, embedding dimension for the quantile input. + double_dqn: boolean, whether to perform double DQN style learning + as described in Van Hasselt et al.: https://arxiv.org/abs/1509.06461. + summary_writer: SummaryWriter object for outputting training statistics. + Summary writing disabled if set to None. + summary_writing_frequency: int, frequency with which summaries will be + written. Lower values will result in slower training. + """ + self.kappa = kappa + # num_tau_samples = N below equation (3) in the paper. + self.num_tau_samples = num_tau_samples + # num_tau_prime_samples = N' below equation (3) in the paper. + self.num_tau_prime_samples = num_tau_prime_samples + # num_quantile_samples = k below equation (3) in the paper. + self.num_quantile_samples = num_quantile_samples + # quantile_embedding_dim = n above equation (4) in the paper. + self.quantile_embedding_dim = quantile_embedding_dim + # option to perform double dqn. + self.double_dqn = double_dqn + + super(ImplicitQuantileAgent, self).__init__( + sess=sess, + num_actions=num_actions, + network=network, + summary_writer=summary_writer, + summary_writing_frequency=summary_writing_frequency) + + def _get_network_type(self): + """Returns the type of the outputs of the implicit quantile network. + + Returns: + _network_type object defining the outputs of the network. + """ + return collections.namedtuple( + 'iqn_network', ['quantile_values', 'quantiles']) + + def _network_template(self, state, num_quantiles): + r"""Builds an Implicit Quantile ConvNet. + + Takes state and quantile as inputs and outputs state-action quantile values. + + Args: + state: A `tf.placeholder` for the RL state. + num_quantiles: int, number of quantile inputs. + + Returns: + _network_type object containing quantile value outputs of the network. + """ + return self.network(self.num_actions, self.quantile_embedding_dim, + self._get_network_type(), state, num_quantiles) + + def _train_step(self): + """Runs a single training step. + + Runs a training op if both: + (1) A minimum number of frames have been added to the replay buffer. + (2) `training_steps` is a multiple of `update_period`. + + Also, syncs weights from online to target network if training steps is a + multiple of target update period. + """ + # Run a train op at the rate of self.update_period if enough training steps + # have been run. This matches the Nature DQN behaviour. + if self._replay.memory.add_count > self.min_replay_history: + if self.training_steps % self.update_period == 0: + self._sess.run(self._train_op) + if (self.summary_writer is not None and + self.training_steps > 0 and + self.training_steps % self.summary_writing_frequency == 0): + summary = self._sess.run(self._merged_summaries) + self.summary_writer.add_summary(summary, self.training_steps) + + if self.training_steps % self.target_update_period == 0: + self._sess.run(self._sync_qt_ops) + + self.training_steps += 1 + + def _build_networks(self): + """Builds the IQN computations needed for acting and training. + + These are: + self.online_convnet: For computing the current state's quantile values. + self.target_convnet: For computing the next state's target quantile + values. + self._net_outputs: The actual quantile values. + self._q_argmax: The action maximizing the current state's Q-values. + self._replay_net_outputs: The replayed states' quantile values. + self._replay_next_target_net_outputs: The replayed next states' target + quantile values. + """ + # Calling online_convnet will generate a new graph as defined in + # self._get_network_template using whatever input is passed, but will always + # share the same weights. + self.online_convnet = tf.make_template('Online', self._network_template) + self.target_convnet = tf.make_template('Target', self._network_template) + + # Compute the Q-values which are used for action selection in the current + # state. + self._net_outputs = self.online_convnet(self.state_ph, + self.num_quantile_samples) + # Shape of self._net_outputs.quantile_values: + # num_quantile_samples x num_actions. + # e.g. if num_actions is 2, it might look something like this: + # Vals for Quantile .2 Vals for Quantile .4 Vals for Quantile .6 + # [[0.1, 0.5], [0.15, -0.3], [0.15, -0.2]] + # Q-values = [(0.1 + 0.15 + 0.15)/3, (0.5 + 0.15 + -0.2)/3]. + self._q_values = tf.reduce_mean(self._net_outputs.quantile_values, axis=0) + self._q_argmax = tf.argmax(self._q_values, axis=0) + + self._replay_net_outputs = self.online_convnet(self._replay.states, + self.num_tau_samples) + # Shape: (num_tau_samples x batch_size) x num_actions. + self._replay_net_quantile_values = self._replay_net_outputs.quantile_values + self._replay_net_quantiles = self._replay_net_outputs.quantiles + + # Do the same for next states in the replay buffer. + self._replay_net_target_outputs = self.target_convnet( + self._replay.next_states, self.num_tau_prime_samples) + # Shape: (num_tau_prime_samples x batch_size) x num_actions. + vals = self._replay_net_target_outputs.quantile_values + self._replay_net_target_quantile_values = vals + + # Compute Q-values which are used for action selection for the next states + # in the replay buffer. Compute the argmax over the Q-values. + if self.double_dqn: + outputs_action = self.online_convnet(self._replay.next_states, + self.num_quantile_samples) + else: + outputs_action = self.target_convnet(self._replay.next_states, + self.num_quantile_samples) + + # Shape: (num_quantile_samples x batch_size) x num_actions. + target_quantile_values_action = outputs_action.quantile_values + # Shape: num_quantile_samples x batch_size x num_actions. + target_quantile_values_action = tf.reshape(target_quantile_values_action, + [self.num_quantile_samples, + self._replay.batch_size, + self.num_actions]) + # Shape: batch_size x num_actions. + self._replay_net_target_q_values = tf.squeeze(tf.reduce_mean( + target_quantile_values_action, axis=0)) + self._replay_next_qt_argmax = tf.argmax( + self._replay_net_target_q_values, axis=1) + + def _build_target_quantile_values_op(self): + """Build an op used as a target for return values at given quantiles. + + Returns: + An op calculating the target quantile return. + """ + batch_size = tf.shape(self._replay.rewards)[0] + # Shape of rewards: (num_tau_prime_samples x batch_size) x 1. + rewards = self._replay.rewards[:, None] + rewards = tf.tile(rewards, [self.num_tau_prime_samples, 1]) + + is_terminal_multiplier = 1. - tf.to_float(self._replay.terminals) + # Incorporate terminal state to discount factor. + # size of gamma_with_terminal: (num_tau_prime_samples x batch_size) x 1. + gamma_with_terminal = self.cumulative_gamma * is_terminal_multiplier + gamma_with_terminal = tf.tile(gamma_with_terminal[:, None], + [self.num_tau_prime_samples, 1]) + + # Get the indices of the maximium Q-value across the action dimension. + # Shape of replay_next_qt_argmax: (num_tau_prime_samples x batch_size) x 1. + + replay_next_qt_argmax = tf.tile( + self._replay_next_qt_argmax[:, None], [self.num_tau_prime_samples, 1]) + + # Shape of batch_indices: (num_tau_prime_samples x batch_size) x 1. + batch_indices = tf.cast(tf.range( + self.num_tau_prime_samples * batch_size)[:, None], tf.int64) + + # Shape of batch_indexed_target_values: + # (num_tau_prime_samples x batch_size) x 2. + batch_indexed_target_values = tf.concat( + [batch_indices, replay_next_qt_argmax], axis=1) + + # Shape of next_target_values: (num_tau_prime_samples x batch_size) x 1. + target_quantile_values = tf.gather_nd( + self._replay_net_target_quantile_values, + batch_indexed_target_values)[:, None] + + return rewards + gamma_with_terminal * target_quantile_values + + def _build_train_op(self): + """Builds a training op. + + Returns: + train_op: An op performing one step of training from replay data. + """ + batch_size = tf.shape(self._replay.rewards)[0] + + target_quantile_values = tf.stop_gradient( + self._build_target_quantile_values_op()) + # Reshape to self.num_tau_prime_samples x batch_size x 1 since this is + # the manner in which the target_quantile_values are tiled. + target_quantile_values = tf.reshape(target_quantile_values, + [self.num_tau_prime_samples, + batch_size, 1]) + # Transpose dimensions so that the dimensionality is batch_size x + # self.num_tau_prime_samples x 1 to prepare for computation of + # Bellman errors. + # Final shape of target_quantile_values: + # batch_size x num_tau_prime_samples x 1. + target_quantile_values = tf.transpose(target_quantile_values, [1, 0, 2]) + + # Shape of indices: (num_tau_samples x batch_size) x 1. + # Expand dimension by one so that it can be used to index into all the + # quantiles when using the tf.gather_nd function (see below). + indices = tf.range(self.num_tau_samples * batch_size)[:, None] + + # Expand the dimension by one so that it can be used to index into all the + # quantiles when using the tf.gather_nd function (see below). + reshaped_actions = self._replay.actions[:, None] + reshaped_actions = tf.tile(reshaped_actions, [self.num_tau_samples, 1]) + # Shape of reshaped_actions: (num_tau_samples x batch_size) x 2. + reshaped_actions = tf.concat([indices, reshaped_actions], axis=1) + + chosen_action_quantile_values = tf.gather_nd( + self._replay_net_quantile_values, reshaped_actions) + # Transpose dimensions so that the dimensionality is batch_size x + # self.num_tau_samples x 1 to prepare for computation of + # Bellman errors. + # Reshape to self.num_tau_samples x batch_size x 1 since this is the manner + # in which the quantile values are tiled. + chosen_action_quantile_values = tf.reshape(chosen_action_quantile_values, + [self.num_tau_samples, + batch_size, 1]) + # Final shape of chosen_action_quantile_values: + # batch_size x num_tau_samples x 1. + chosen_action_quantile_values = tf.transpose( + chosen_action_quantile_values, [1, 0, 2]) #batchsize x quan x 1 + + # Shape of bellman_erors and huber_loss: + # batch_size x num_tau_prime_samples x num_tau_samples x 1. + bellman_errors = target_quantile_values[:, :, None, :] - chosen_action_quantile_values[:, None, :, :] + # The huber loss (see Section 2.3 of the paper) is defined via two cases: + # case_one: |bellman_errors| <= kappa + # case_two: |bellman_errors| > kappa + huber_loss_case_one = tf.to_float( + tf.abs(bellman_errors) <= self.kappa) * 0.5 * bellman_errors ** 2 + huber_loss_case_two = tf.to_float( + tf.abs(bellman_errors) > self.kappa) * self.kappa * ( + tf.abs(bellman_errors) - 0.5 * self.kappa) + huber_loss = huber_loss_case_one + huber_loss_case_two + + # Reshape replay_quantiles to batch_size x num_tau_samples x 1 + replay_quantiles = tf.reshape( + self._replay_net_quantiles, [self.num_tau_samples, batch_size, 1]) + replay_quantiles = tf.transpose(replay_quantiles, [1, 0, 2]) #batchsize x quan x 1 + + # Tile by num_tau_prime_samples along a new dimension. Shape is now + # batch_size x num_tau_prime_samples x num_tau_samples x 1. + # These quantiles will be used for computation of the quantile huber loss + # below (see section 2.3 of the paper). + replay_quantiles = tf.to_float(tf.tile( + replay_quantiles[:, None, :, :], [1, self.num_tau_prime_samples, 1, 1])) + # Shape: batch_size x num_tau_prime_samples x num_tau_samples x 1. + quantile_huber_loss = (tf.abs(tf.stop_gradient(replay_quantiles) - tf.stop_gradient( + tf.to_float(bellman_errors < 0))) * huber_loss) / self.kappa + # Sum over current quantile value (num_tau_samples) dimension, + # average over target quantile value (num_tau_prime_samples) dimension. + # Shape: batch_size x num_tau_prime_samples x 1. + loss = tf.reduce_sum(quantile_huber_loss, axis=2) + # Shape: batch_size x 1. + loss = tf.reduce_mean(loss, axis=1) + + # TODO(kumasaurabh): Add prioritized replay functionality here. + update_priorities_op = tf.no_op() + with tf.control_dependencies([update_priorities_op]): + if self.summary_writer is not None: + with tf.variable_scope('Losses'): + tf.summary.scalar('QuantileLoss', tf.reduce_mean(loss)) + return self.optimizer.minimize(tf.reduce_mean(loss)) diff --git a/dopamine/agents/rainbow/__init__.py b/dopamine/agents/rainbow/__init__.py index 920cbb5..dc108ba 100644 --- a/dopamine/agents/rainbow/__init__.py +++ b/dopamine/agents/rainbow/__init__.py @@ -1,15 +1,15 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/dopamine/agents/rainbow/configs/c51.gin b/dopamine/agents/rainbow/configs/c51.gin index 495e82d..8d76503 100644 --- a/dopamine/agents/rainbow/configs/c51.gin +++ b/dopamine/agents/rainbow/configs/c51.gin @@ -1,42 +1,42 @@ -# Hyperparameters follow the settings from Bellemare et al. (2017), but we -# modify as necessary to match those used in Rainbow (Hessel et al., 2018), to -# ensure apples-to-apples comparison. -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -RainbowAgent.num_atoms = 51 -RainbowAgent.dueltype = 'DUELTYPE' -RainbowAgent.game = 'GAME' -RainbowAgent.vmax = 10. -RainbowAgent.gamma = 0.99 -RainbowAgent.update_horizon = 1 -RainbowAgent.min_replay_history = 20000 # agent steps -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 8000 # agent steps -RainbowAgent.epsilon_train = 0.01 -RainbowAgent.epsilon_eval = 0.001 -RainbowAgent.epsilon_decay_period = 250000 # agent steps -RainbowAgent.replay_scheme = 'uniform' -RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.00025 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -atari_lib.create_atari_environment.game_name = 'GAME' #'Pong' -# Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). -#atari_lib.create_atari_environment.sticky_actions = True -atari_lib.create_atari_environment.sticky_actions = False -create_agent.agent_name = 'rainbow' -Runner.num_iterations = 200 -Runner.dueltype = 'DUELTYPE' -Runner.game = 'GAME' -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps - -WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 -WrappedPrioritizedReplayBuffer.batch_size = 32 +# Hyperparameters follow the settings from Bellemare et al. (2017), but we +# modify as necessary to match those used in Rainbow (Hessel et al., 2018), to +# ensure apples-to-apples comparison. +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +RainbowAgent.num_atoms = 51 +RainbowAgent.dueltype = 'DUELTYPE' +RainbowAgent.game = 'GAME' +RainbowAgent.vmax = 10. +RainbowAgent.gamma = 0.99 +RainbowAgent.update_horizon = 1 +RainbowAgent.min_replay_history = 20000 # agent steps +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 8000 # agent steps +RainbowAgent.epsilon_train = 0.01 +RainbowAgent.epsilon_eval = 0.001 +RainbowAgent.epsilon_decay_period = 250000 # agent steps +RainbowAgent.replay_scheme = 'uniform' +RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.00025 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +atari_lib.create_atari_environment.game_name = 'GAME' #'Pong' +# Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). +#atari_lib.create_atari_environment.sticky_actions = True +atari_lib.create_atari_environment.sticky_actions = False +create_agent.agent_name = 'rainbow' +Runner.num_iterations = 200 +Runner.dueltype = 'DUELTYPE' +Runner.game = 'GAME' +Runner.training_steps = 250000 # agent steps +Runner.evaluation_steps = 125000 # agent steps +Runner.max_steps_per_episode = 27000 # agent steps + +WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 +WrappedPrioritizedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/rainbow/configs/c51_acrobot.gin b/dopamine/agents/rainbow/configs/c51_acrobot.gin index 9acd3b5..aec72cb 100644 --- a/dopamine/agents/rainbow/configs/c51_acrobot.gin +++ b/dopamine/agents/rainbow/configs/c51_acrobot.gin @@ -1,39 +1,39 @@ -# Hyperparameters for a simple C51-style Acrobot agent. The hyperparameters -# chosen achieve reasonable performance. -import dopamine.agents.dqn.dqn_agent -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.gym_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -RainbowAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE -RainbowAgent.observation_dtype = %gym_lib.ACROBOT_OBSERVATION_DTYPE -RainbowAgent.stack_size = %gym_lib.ACROBOT_STACK_SIZE -RainbowAgent.network = @gym_lib.acrobot_rainbow_network -RainbowAgent.num_atoms = 51 -RainbowAgent.vmax = 10. -RainbowAgent.gamma = 0.99 -RainbowAgent.update_horizon = 1 -RainbowAgent.min_replay_history = 500 -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 100 -RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon -RainbowAgent.replay_scheme = 'uniform' -RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.1 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -create_gym_environment.environment_name = 'Acrobot' -create_gym_environment.version = 'v1' -create_agent.agent_name = 'rainbow' -Runner.create_environment_fn = @gym_lib.create_gym_environment -Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 500 - -WrappedPrioritizedReplayBuffer.replay_capacity = 50000 -WrappedPrioritizedReplayBuffer.batch_size = 128 +# Hyperparameters for a simple C51-style Acrobot agent. The hyperparameters +# chosen achieve reasonable performance. +import dopamine.agents.dqn.dqn_agent +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.gym_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +RainbowAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE +RainbowAgent.observation_dtype = %gym_lib.ACROBOT_OBSERVATION_DTYPE +RainbowAgent.stack_size = %gym_lib.ACROBOT_STACK_SIZE +RainbowAgent.network = @gym_lib.acrobot_rainbow_network +RainbowAgent.num_atoms = 51 +RainbowAgent.vmax = 10. +RainbowAgent.gamma = 0.99 +RainbowAgent.update_horizon = 1 +RainbowAgent.min_replay_history = 500 +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 100 +RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon +RainbowAgent.replay_scheme = 'uniform' +RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.1 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +create_gym_environment.environment_name = 'Acrobot' +create_gym_environment.version = 'v1' +create_agent.agent_name = 'rainbow' +Runner.create_environment_fn = @gym_lib.create_gym_environment +Runner.num_iterations = 500 +Runner.training_steps = 1000 +Runner.evaluation_steps = 1000 +Runner.max_steps_per_episode = 500 + +WrappedPrioritizedReplayBuffer.replay_capacity = 50000 +WrappedPrioritizedReplayBuffer.batch_size = 128 diff --git a/dopamine/agents/rainbow/configs/c51_cartpole.gin b/dopamine/agents/rainbow/configs/c51_cartpole.gin index 16390e2..5bbf081 100644 --- a/dopamine/agents/rainbow/configs/c51_cartpole.gin +++ b/dopamine/agents/rainbow/configs/c51_cartpole.gin @@ -1,39 +1,39 @@ -# Hyperparameters for a simple C51-style Cartpole agent. The hyperparameters -# chosen achieve reasonable performance. -import dopamine.agents.dqn.dqn_agent -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.gym_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE -RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE -RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE -RainbowAgent.network = @gym_lib.cartpole_rainbow_network -RainbowAgent.num_atoms = 51 -RainbowAgent.vmax = 10. -RainbowAgent.gamma = 0.99 -RainbowAgent.update_horizon = 1 -RainbowAgent.min_replay_history = 500 -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 100 -RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon -RainbowAgent.replay_scheme = 'uniform' -RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.001 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -create_gym_environment.environment_name = 'CartPole' -create_gym_environment.version = 'v0' -create_agent.agent_name = 'rainbow' -Runner.create_environment_fn = @gym_lib.create_gym_environment -Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 200 # Default max episode length. - -WrappedPrioritizedReplayBuffer.replay_capacity = 50000 -WrappedPrioritizedReplayBuffer.batch_size = 128 +# Hyperparameters for a simple C51-style Cartpole agent. The hyperparameters +# chosen achieve reasonable performance. +import dopamine.agents.dqn.dqn_agent +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.gym_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE +RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE +RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE +RainbowAgent.network = @gym_lib.cartpole_rainbow_network +RainbowAgent.num_atoms = 51 +RainbowAgent.vmax = 10. +RainbowAgent.gamma = 0.99 +RainbowAgent.update_horizon = 1 +RainbowAgent.min_replay_history = 500 +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 100 +RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon +RainbowAgent.replay_scheme = 'uniform' +RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.001 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +create_gym_environment.environment_name = 'CartPole' +create_gym_environment.version = 'v0' +create_agent.agent_name = 'rainbow' +Runner.create_environment_fn = @gym_lib.create_gym_environment +Runner.num_iterations = 500 +Runner.training_steps = 1000 +Runner.evaluation_steps = 1000 +Runner.max_steps_per_episode = 200 # Default max episode length. + +WrappedPrioritizedReplayBuffer.replay_capacity = 50000 +WrappedPrioritizedReplayBuffer.batch_size = 128 diff --git a/dopamine/agents/rainbow/configs/c51_icml.gin b/dopamine/agents/rainbow/configs/c51_icml.gin index 3e18b7f..a3eae26 100644 --- a/dopamine/agents/rainbow/configs/c51_icml.gin +++ b/dopamine/agents/rainbow/configs/c51_icml.gin @@ -1,41 +1,41 @@ -# Hyperparameters used in Bellemare et al. (2017). -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -RainbowAgent.num_atoms = 51 -RainbowAgent.dueltype = 'DUELTYPE' -RainbowAgent.game = 'GAME' -RainbowAgent.vmax = 10. -RainbowAgent.gamma = 0.99 -RainbowAgent.update_horizon = 1 -RainbowAgent.min_replay_history = 50000 # agent steps -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 10000 # agent steps -RainbowAgent.epsilon_train = 0.01 -RainbowAgent.epsilon_eval = 0.001 -RainbowAgent.epsilon_decay_period = 1000000 # agent steps -RainbowAgent.replay_scheme = 'uniform' -RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.00025 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -atari_lib.create_atari_environment.game_name = 'GAME' #'Pong' -# Deterministic ALE version used in the DQN Nature paper (Mnih et al., 2015). -atari_lib.create_atari_environment.sticky_actions = False -create_agent.agent_name = 'rainbow' -Runner.num_iterations = 200 -Runner.dueltype = 'DUELTYPE' -Runner.game = 'GAME' -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps - -AtariPreprocessing.terminal_on_life_loss = True - -WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 -WrappedPrioritizedReplayBuffer.batch_size = 32 +# Hyperparameters used in Bellemare et al. (2017). +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +RainbowAgent.num_atoms = 51 +RainbowAgent.dueltype = 'DUELTYPE' +RainbowAgent.game = 'GAME' +RainbowAgent.vmax = 10. +RainbowAgent.gamma = 0.99 +RainbowAgent.update_horizon = 1 +RainbowAgent.min_replay_history = 50000 # agent steps +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 10000 # agent steps +RainbowAgent.epsilon_train = 0.01 +RainbowAgent.epsilon_eval = 0.001 +RainbowAgent.epsilon_decay_period = 1000000 # agent steps +RainbowAgent.replay_scheme = 'uniform' +RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.00025 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +atari_lib.create_atari_environment.game_name = 'GAME' #'Pong' +# Deterministic ALE version used in the DQN Nature paper (Mnih et al., 2015). +atari_lib.create_atari_environment.sticky_actions = False +create_agent.agent_name = 'rainbow' +Runner.num_iterations = 200 +Runner.dueltype = 'DUELTYPE' +Runner.game = 'GAME' +Runner.training_steps = 250000 # agent steps +Runner.evaluation_steps = 125000 # agent steps +Runner.max_steps_per_episode = 27000 # agent steps + +AtariPreprocessing.terminal_on_life_loss = True + +WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 +WrappedPrioritizedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/rainbow/configs/rainbow.gin b/dopamine/agents/rainbow/configs/rainbow.gin index 774cfd8..9387572 100644 --- a/dopamine/agents/rainbow/configs/rainbow.gin +++ b/dopamine/agents/rainbow/configs/rainbow.gin @@ -1,42 +1,42 @@ -# Hyperparameters follow Hessel et al. (2018), except for sticky_actions, -# which was False (not using sticky actions) in the original paper. -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -RainbowAgent.num_atoms = 51 -RainbowAgent.runtype = 'RUNTYPE' -RainbowAgent.game = 'GAME' -RainbowAgent.vmax = 10. -RainbowAgent.gamma = 0.99 -RainbowAgent.update_horizon = 3 -RainbowAgent.min_replay_history = 20000 # agent steps -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 8000 # agent steps -RainbowAgent.epsilon_train = 0.01 -RainbowAgent.epsilon_eval = 0.001 -RainbowAgent.epsilon_decay_period = 250000 # agent steps -RainbowAgent.replay_scheme = 'prioritized' -RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -# Note these parameters are different from C51's. -tf.train.AdamOptimizer.learning_rate = 0.0000625 -tf.train.AdamOptimizer.epsilon = 0.00015 - -atari_lib.create_atari_environment.game_name = 'GAME' #'Pong' -# Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). -#atari_lib.create_atari_environment.sticky_actions = True -atari_lib.create_atari_environment.sticky_actions = False -create_agent.agent_name = 'rainbow' -Runner.num_iterations = 200 -Runner.runtype = 'RUNTYPE' -Runner.game = 'GAME' -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps - -WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 -WrappedPrioritizedReplayBuffer.batch_size = 32 +# Hyperparameters follow Hessel et al. (2018), except for sticky_actions, +# which was False (not using sticky actions) in the original paper. +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +RainbowAgent.num_atoms = 51 +RainbowAgent.runtype = 'RUNTYPE' +RainbowAgent.game = 'GAME' +RainbowAgent.vmax = 10. +RainbowAgent.gamma = 0.99 +RainbowAgent.update_horizon = 3 +RainbowAgent.min_replay_history = 20000 # agent steps +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 8000 # agent steps +RainbowAgent.epsilon_train = 0.01 +RainbowAgent.epsilon_eval = 0.001 +RainbowAgent.epsilon_decay_period = 250000 # agent steps +RainbowAgent.replay_scheme = 'prioritized' +RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +# Note these parameters are different from C51's. +tf.train.AdamOptimizer.learning_rate = 0.0000625 +tf.train.AdamOptimizer.epsilon = 0.00015 + +atari_lib.create_atari_environment.game_name = 'GAME' #'Pong' +# Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). +#atari_lib.create_atari_environment.sticky_actions = True +atari_lib.create_atari_environment.sticky_actions = False +create_agent.agent_name = 'rainbow' +Runner.num_iterations = 200 +Runner.runtype = 'RUNTYPE' +Runner.game = 'GAME' +Runner.training_steps = 250000 # agent steps +Runner.evaluation_steps = 125000 # agent steps +Runner.max_steps_per_episode = 27000 # agent steps + +WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 +WrappedPrioritizedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/rainbow/configs/rainbow_aaai.gin b/dopamine/agents/rainbow/configs/rainbow_aaai.gin index a4af67b..237d272 100644 --- a/dopamine/agents/rainbow/configs/rainbow_aaai.gin +++ b/dopamine/agents/rainbow/configs/rainbow_aaai.gin @@ -1,43 +1,43 @@ -# Hyperparameters follow Hessel et al. (2018). -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.atari_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables -import os - -RainbowAgent.num_atoms = 51 -RainbowAgent.runtype = 'RUNTYPE' -RainbowAgent.game = 'GAME' -RainbowAgent.vmax = 10. -RainbowAgent.gamma = 0.99 -RainbowAgent.update_horizon = 1 -RainbowAgent.min_replay_history = 20000 # agent steps -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 8000 # agent steps -RainbowAgent.epsilon_train = 0.01 -RainbowAgent.epsilon_eval = 0.001 -RainbowAgent.epsilon_decay_period = 250000 # agent steps -#RainbowAgent.replay_scheme = 'prioritized' -RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -# Note these parameters are different from C51's. -tf.train.AdamOptimizer.learning_rate = 0.0000625 -tf.train.AdamOptimizer.epsilon = 0.00015 - -atari_lib.create_atari_environment.game_name = 'GAME' #'StarGunner' -# Deterministic ALE version used in the AAAI paper. -atari_lib.create_atari_environment.sticky_actions = False -create_agent.agent_name = 'rainbow' -Runner.num_iterations = 200 -Runner.runtype = 'RUNTYPE' -Runner.game = 'GAME' -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps - -AtariPreprocessing.terminal_on_life_loss = True - -WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 -WrappedPrioritizedReplayBuffer.batch_size = 32 +# Hyperparameters follow Hessel et al. (2018). +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables +import os + +RainbowAgent.num_atoms = 51 +RainbowAgent.runtype = 'RUNTYPE' +RainbowAgent.game = 'GAME' +RainbowAgent.vmax = 10. +RainbowAgent.gamma = 0.99 +RainbowAgent.update_horizon = 1 +RainbowAgent.min_replay_history = 20000 # agent steps +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 8000 # agent steps +RainbowAgent.epsilon_train = 0.01 +RainbowAgent.epsilon_eval = 0.001 +RainbowAgent.epsilon_decay_period = 250000 # agent steps +#RainbowAgent.replay_scheme = 'prioritized' +RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +# Note these parameters are different from C51's. +tf.train.AdamOptimizer.learning_rate = 0.0000625 +tf.train.AdamOptimizer.epsilon = 0.00015 + +atari_lib.create_atari_environment.game_name = 'GAME' #'StarGunner' +# Deterministic ALE version used in the AAAI paper. +atari_lib.create_atari_environment.sticky_actions = False +create_agent.agent_name = 'rainbow' +Runner.num_iterations = 200 +Runner.runtype = 'RUNTYPE' +Runner.game = 'GAME' +Runner.training_steps = 250000 # agent steps +Runner.evaluation_steps = 125000 # agent steps +Runner.max_steps_per_episode = 27000 # agent steps + +AtariPreprocessing.terminal_on_life_loss = True + +WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 +WrappedPrioritizedReplayBuffer.batch_size = 32 diff --git a/dopamine/agents/rainbow/configs/rainbow_acrobot.gin b/dopamine/agents/rainbow/configs/rainbow_acrobot.gin index e58ef8b..5442e79 100644 --- a/dopamine/agents/rainbow/configs/rainbow_acrobot.gin +++ b/dopamine/agents/rainbow/configs/rainbow_acrobot.gin @@ -1,38 +1,38 @@ -# Hyperparameters for a simple Rainbow-style Acrobot agent. The hyperparameters -# chosen achieve reasonable performance. -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.gym_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -RainbowAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE -RainbowAgent.observation_dtype = %gym_lib.ACROBOT_OBSERVATION_DTYPE -RainbowAgent.stack_size = %gym_lib.ACROBOT_STACK_SIZE -RainbowAgent.network = @gym_lib.acrobot_rainbow_network -RainbowAgent.num_atoms = 51 -RainbowAgent.vmax = 10. -RainbowAgent.gamma = 0.99 -RainbowAgent.update_horizon = 3 -RainbowAgent.min_replay_history = 500 -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 100 -RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon -RainbowAgent.replay_scheme = 'prioritized' -RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.09 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -create_gym_environment.environment_name = 'Acrobot' -create_gym_environment.version = 'v1' -create_agent.agent_name = 'rainbow' -Runner.create_environment_fn = @gym_lib.create_gym_environment -Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 500 - -WrappedPrioritizedReplayBuffer.replay_capacity = 50000 -WrappedPrioritizedReplayBuffer.batch_size = 128 +# Hyperparameters for a simple Rainbow-style Acrobot agent. The hyperparameters +# chosen achieve reasonable performance. +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.gym_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +RainbowAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE +RainbowAgent.observation_dtype = %gym_lib.ACROBOT_OBSERVATION_DTYPE +RainbowAgent.stack_size = %gym_lib.ACROBOT_STACK_SIZE +RainbowAgent.network = @gym_lib.acrobot_rainbow_network +RainbowAgent.num_atoms = 51 +RainbowAgent.vmax = 10. +RainbowAgent.gamma = 0.99 +RainbowAgent.update_horizon = 3 +RainbowAgent.min_replay_history = 500 +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 100 +RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon +RainbowAgent.replay_scheme = 'prioritized' +RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.09 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +create_gym_environment.environment_name = 'Acrobot' +create_gym_environment.version = 'v1' +create_agent.agent_name = 'rainbow' +Runner.create_environment_fn = @gym_lib.create_gym_environment +Runner.num_iterations = 500 +Runner.training_steps = 1000 +Runner.evaluation_steps = 1000 +Runner.max_steps_per_episode = 500 + +WrappedPrioritizedReplayBuffer.replay_capacity = 50000 +WrappedPrioritizedReplayBuffer.batch_size = 128 diff --git a/dopamine/agents/rainbow/configs/rainbow_cartpole.gin b/dopamine/agents/rainbow/configs/rainbow_cartpole.gin index 3f4c3dc..3c3d9db 100644 --- a/dopamine/agents/rainbow/configs/rainbow_cartpole.gin +++ b/dopamine/agents/rainbow/configs/rainbow_cartpole.gin @@ -1,39 +1,39 @@ -# Hyperparameters for a simple Rainbow-style Cartpole agent. The -# hyperparameters chosen achieve reasonable performance. -import dopamine.agents.dqn.dqn_agent -import dopamine.agents.rainbow.rainbow_agent -import dopamine.discrete_domains.gym_lib -import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer -import gin.tf.external_configurables - -RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE -RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE -RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE -RainbowAgent.network = @gym_lib.cartpole_rainbow_network -RainbowAgent.num_atoms = 51 -RainbowAgent.vmax = 10. -RainbowAgent.gamma = 0.99 -RainbowAgent.update_horizon = 3 -RainbowAgent.min_replay_history = 500 -RainbowAgent.update_period = 4 -RainbowAgent.target_update_period = 100 -RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon -RainbowAgent.replay_scheme = 'prioritized' -RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version -RainbowAgent.optimizer = @tf.train.AdamOptimizer() - -tf.train.AdamOptimizer.learning_rate = 0.09 -tf.train.AdamOptimizer.epsilon = 0.0003125 - -create_gym_environment.environment_name = 'CartPole' -create_gym_environment.version = 'v0' -create_agent.agent_name = 'rainbow' -Runner.create_environment_fn = @gym_lib.create_gym_environment -Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 200 # Default max episode length. - -WrappedPrioritizedReplayBuffer.replay_capacity = 50000 -WrappedPrioritizedReplayBuffer.batch_size = 128 +# Hyperparameters for a simple Rainbow-style Cartpole agent. The +# hyperparameters chosen achieve reasonable performance. +import dopamine.agents.dqn.dqn_agent +import dopamine.agents.rainbow.rainbow_agent +import dopamine.discrete_domains.gym_lib +import dopamine.discrete_domains.run_experiment +import dopamine.replay_memory.prioritized_replay_buffer +import gin.tf.external_configurables + +RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE +RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE +RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE +RainbowAgent.network = @gym_lib.cartpole_rainbow_network +RainbowAgent.num_atoms = 51 +RainbowAgent.vmax = 10. +RainbowAgent.gamma = 0.99 +RainbowAgent.update_horizon = 3 +RainbowAgent.min_replay_history = 500 +RainbowAgent.update_period = 4 +RainbowAgent.target_update_period = 100 +RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon +RainbowAgent.replay_scheme = 'prioritized' +RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version +RainbowAgent.optimizer = @tf.train.AdamOptimizer() + +tf.train.AdamOptimizer.learning_rate = 0.09 +tf.train.AdamOptimizer.epsilon = 0.0003125 + +create_gym_environment.environment_name = 'CartPole' +create_gym_environment.version = 'v0' +create_agent.agent_name = 'rainbow' +Runner.create_environment_fn = @gym_lib.create_gym_environment +Runner.num_iterations = 500 +Runner.training_steps = 1000 +Runner.evaluation_steps = 1000 +Runner.max_steps_per_episode = 200 # Default max episode length. + +WrappedPrioritizedReplayBuffer.replay_capacity = 50000 +WrappedPrioritizedReplayBuffer.batch_size = 128 diff --git a/dopamine/agents/rainbow/rainbow_agent.py b/dopamine/agents/rainbow/rainbow_agent.py index 3abdc26..3e81cda 100644 --- a/dopamine/agents/rainbow/rainbow_agent.py +++ b/dopamine/agents/rainbow/rainbow_agent.py @@ -1,939 +1,939 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Compact implementation of a simplified Rainbow agent. - -Specifically, we implement the following components from Rainbow: - - * n-step updates; - * prioritized replay; and - * distributional RL. - -These three components were found to significantly impact the performance of -the Atari game-playing agent. - -Furthermore, our implementation does away with some minor hyperparameter -choices. Specifically, we - - * keep the beta exponent fixed at beta=0.5, rather than increase it linearly; - * remove the alpha parameter, which was set to alpha=0.5 throughout the paper. - -Details in "Rainbow: Combining Improvements in Deep Reinforcement Learning" by -Hessel et al. (2018). -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import csv, json, pickle -import multiprocessing - - - -from dopamine.agents.dqn import dqn_agent -from dopamine.discrete_domains import atari_lib -from dopamine.replay_memory import prioritized_replay_buffer -import tensorflow as tf -import random -import numpy as np - -import gin.tf - -slim = tf.contrib.slim - - -@gin.configurable -class RainbowAgent(dqn_agent.DQNAgent): - """A compact implementation of a simplified Rainbow agent.""" - - def __init__(self, - sess, - num_actions, - observation_shape=dqn_agent.NATURE_DQN_OBSERVATION_SHAPE, - observation_dtype=dqn_agent.NATURE_DQN_DTYPE, - stack_size=dqn_agent.NATURE_DQN_STACK_SIZE, - network=atari_lib.rainbow_network, - runtype='run', - game=None, - num_atoms=51, - vmax=10., - gamma=0.99, - update_horizon=1, - min_replay_history=20000, - update_period=4, - target_update_period=8000, - epsilon_fn=dqn_agent.linearly_decaying_epsilon, - epsilon_train=0.01, - epsilon_eval=0.001, - epsilon_decay_period=250000, - replay_scheme='prioritized', - tf_device='/cpu:*', - use_staging=True, - optimizer=tf.train.AdamOptimizer( - learning_rate=0.00025, epsilon=0.0003125), - summary_writer=None, - summary_writing_frequency=500): - """Initializes the agent and constructs the components of its graph. - - Args: - sess: `tf.Session`, for executing ops. - num_actions: int, number of actions the agent can take at any state. - observation_shape: tuple of ints or an int. If single int, the observation - is assumed to be a 2D square. - observation_dtype: tf.DType, specifies the type of the observations. Note - that if your inputs are continuous, you should set this to tf.float32. - stack_size: int, number of frames to use in state stack. - network: function expecting three parameters: - (num_actions, network_type, state). This function will return the - network_type object containing the tensors output by the network. - See dopamine.discrete_domains.atari_lib.rainbow_network as - an example. - num_atoms: int, the number of buckets of the value function distribution. - vmax: float, the value distribution support is [-vmax, vmax]. - gamma: float, discount factor with the usual RL meaning. - update_horizon: int, horizon at which updates are performed, the 'n' in - n-step update. - min_replay_history: int, number of transitions that should be experienced - before the agent begins training its value function. - update_period: int, period between DQN updates. - target_update_period: int, update period for the target network. - epsilon_fn: function expecting 4 parameters: - (decay_period, step, warmup_steps, epsilon). This function should return - the epsilon value used for exploration during training. - epsilon_train: float, the value to which the agent's epsilon is eventually - decayed during training. - epsilon_eval: float, epsilon used when evaluating the agent. - epsilon_decay_period: int, length of the epsilon decay schedule. - replay_scheme: str, 'prioritized' or 'uniform', the sampling scheme of the - replay memory. - tf_device: str, Tensorflow device on which the agent's graph is executed. - use_staging: bool, when True use a staging area to prefetch the next - training batch, speeding training up by about 30%. - optimizer: `tf.train.Optimizer`, for training the value function. - summary_writer: SummaryWriter object for outputting training statistics. - Summary writing disabled if set to None. - summary_writing_frequency: int, frequency with which summaries will be - written. Lower values will result in slower training. - """ - self.ACTION_MEANING = { - 0: "NOOP", - 1: "FIRE", - 2: "UP", - 3: "RIGHT", - 4: "LEFT", - 5: "DOWN", - 6: "UPRIGHT", - 7: "UPLEFT", - 8: "DOWNRIGHT", - 9: "DOWNLEFT", - 10: "UPFIRE", - 11: "RIGHTFIRE", - 12: "LEFTFIRE", - 13: "DOWNFIRE", - 14: "UPRIGHTFIRE", - 15: "UPLEFTFIRE", - 16: "DOWNRIGHTFIRE", - 17: "DOWNLEFTFIRE", - } - # We need this because some tools convert round floats into ints. - self.testing = False - vmax = float(vmax) - self.N = 1 - self.index = None - self.big_z = None - self.big_a = None - self.big_qv = None - self.unique_num = None - self.v_sup_tensor = None #[tf.constant(vv, dtype=tf.float32) for vv in v_sup_] - self.M = self.sp_a = self.sortsp_a = None - if 'rainbow' in runtype: - num_atoms = int(runtype.split('-')[0][7:]) - num_atoms_sub = self._num_atoms_sub = num_atoms - elif 'c51' in runtype: - num_atoms_sub = self._num_atoms_sub = 51 - else: - num_atoms_sub = num_atoms - self._num_atoms = num_atoms - self._runtype = runtype - self._support = tf.linspace(-vmax, vmax, num_atoms) - self.v_sup_ = np.array(np.linspace(-vmax, vmax, num_atoms)) - self.v_support = tf.linspace(-vmax, vmax, num_atoms_sub) - self.a_support = tf.linspace(-vmax, vmax, num_atoms_sub) #V + k * A - self._game = game - self._klfactor = 0.0 - self._entfactor = 0 - self._ckfactor = 0 - self._num_actions = num_actions - print ("NUM_ATOMS:", num_atoms) - self.q_sup = [] - for i in range(self.N): - self.q_sup.append(np.reshape(np.linspace(-10, 10, num_atoms), (1, num_atoms))) - print ("RUNTYPE:", runtype) - print ("VMAX:", vmax) - print (">>>>>>>>", multiprocessing.cpu_count()) - self.k = 1.0 - print ("-------kkkkk =", self.k) - self.v_support = tf.reshape(self.v_support, (1, 1, num_atoms_sub)) - self.a_support = tf.reshape(self.a_support, (1, 1, num_atoms_sub)) - - self._replay_scheme = replay_scheme - self._filename = './fout-visual/visual-%s_%s.pkl' % (self._game, self._runtype) - self._filename_vis = './vis-%s_%s.pkl' % (self._game, self._runtype) - print ('==========================', self._filename) - self.dict = {"training_step": [], - "state": [], - "prob": [], - "loss": [], - "klloss": [] - } - # TODO(b/110897128): Make agent optimizer attribute private. - self.vis = {"sup": [], "v": [], "s": [], "a": [], "supF": [], "F": [], "r": [], "state_input":[], "gradient":[], - "fraction": [], 'values': []} - self.vis['sup'] = self.q_sup #self.v_sup_ - self.vis['supF'] = self.v_sup_ - self.optimizer = optimizer - - dqn_agent.DQNAgent.__init__( - self, - sess=sess, - num_actions=num_actions, - observation_shape=observation_shape, - observation_dtype=observation_dtype, - stack_size=stack_size, - network=network, - gamma=gamma, - update_horizon=update_horizon, - min_replay_history=min_replay_history, - update_period=update_period, - target_update_period=target_update_period, - epsilon_fn=epsilon_fn, - epsilon_train=epsilon_train, - epsilon_eval=epsilon_eval, - epsilon_decay_period=epsilon_decay_period, - tf_device=tf_device, - use_staging=use_staging, - optimizer=self.optimizer, - summary_writer=summary_writer, - summary_writing_frequency=summary_writing_frequency) - print (self._sess.run(self._support)) - #exit(0) - - def _get_network_type(self): - """Returns the type of the outputs of a value distribution network. - - Returns: - net_type: _network_type object defining the outputs of the network. - """ - return collections.namedtuple('c51_network', - ['q_values', 'logits', 'probabilities', 'v_', 'a', 'q_v', 'a_origin', 'Ea', 'q_support', 'a_support', 'q_values_sub', 'state_input']) - - def _build_networks(self): - """Builds the Q-value network computations needed for acting and training. - - These are: - self.online_convnet: For computing the current state's Q-values. - self.target_convnet: For computing the next state's target Q-values. - self._net_outputs: The actual Q-values. - self._q_argmax: The action maximizing the current state's Q-values. - self._replay_net_outputs: The replayed states' Q-values. - self._replay_next_target_net_outputs: The replayed next states' target - Q-values (see Mnih et al., 2015 for details). - """ - # Calling online_convnet will generate a new graph as defined in - # self._get_network_template using whatever input is passed, but will always - # share the same weights. - self.online_convnet = tf.make_template('Online', self._network_template) - self.target_convnet = tf.make_template('Target', self._network_template) - self._net_outputs = self.online_convnet(self.state_ph) - # TODO(bellemare): Ties should be broken. They are unlikely to happen when - # using a deep network, but may affect performance with a linear - # approximation scheme. - self._q_argmax = tf.argmax(self._net_outputs.q_values, axis=1)[0] - self.gradient = [] - for q_sub in self._net_outputs.q_values_sub: - print (self._q_argmax.shape) - chosen_action_q_sub = q_sub[0][self._q_argmax] ##Z_1(a), Z_2(a) - print ("chosen_action_q_sub:", chosen_action_q_sub.shape) - self.gradient.append(tf.gradients(chosen_action_q_sub, [self._net_outputs.state_input])) - - self._replay_net_outputs = self.online_convnet(self._replay.states) - self._replay_next_target_net_outputs = self.target_convnet( - self._replay.next_states) - - def _network_template(self, state): - """Builds a convolutional network that outputs Q-value distributions. - - Args: - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - return self.network(self.num_actions, self._num_atoms, self._num_atoms_sub, self._support, - self._get_network_type(), state, self._runtype, - self.v_support, self.a_support, self.big_z, self.big_a, self.big_qv, self.N, self.index, self.M, self.sp_a, self.unique_num, self.sortsp_a, self.v_sup_tensor) - #self.v_support, self.a_support, self.big_z, self.idx_matrix_add_onehot, self.idx_matrix_minus_onehot) - - def _build_replay_buffer(self, use_staging): - """Creates the replay buffer used by the agent. - - Args: - use_staging: bool, if True, uses a staging area to prefetch data for - faster training. - - Returns: - A `WrappedPrioritizedReplayBuffer` object. - - Raises: - ValueError: if given an invalid replay scheme. - """ - if self._replay_scheme not in ['uniform', 'prioritized']: - raise ValueError('Invalid replay scheme: {}'.format(self._replay_scheme)) - return prioritized_replay_buffer.WrappedPrioritizedReplayBuffer( - observation_shape=self.observation_shape, - stack_size=self.stack_size, - use_staging=use_staging, - update_horizon=self.update_horizon, - gamma=self.gamma) - - def step(self, reward, observation): - """Records the most recent transition and returns the agent's next action. - - We store the observation of the last time step since we want to store it - with the reward. - - Args: - reward: float, the reward received from the agent's most recent action. - observation: numpy array, the most recent observation. - - Returns: - int, the selected action. - """ - self._last_observation = self._observation - self._record_observation(observation) - - if not self.eval_mode: - self._store_transition(self._last_observation, self.action, reward, False) - self._train_step() - - self.action = self._select_action() - return self.action - - def _train_step(self): - """Runs a single training step. - - Runs a training op if both: - (1) A minimum number of frames have been added to the replay buffer. - (2) `training_steps` is a multiple of `update_period`. - - Also, syncs weights from online to target network if training steps is a - multiple of target update period. - """ - # Run a train op at the rate of self.update_period if enough training steps - # have been run. This matches the Nature DQN behaviour. - if self._replay.memory.add_count > self.min_replay_history: - if self.training_steps % self.update_period == 0: - if 'iqn' in self._runtype: - self._sess.run(self._train_op) - else: - q_sup = None; a_sup = None - pv, pa = None, None - a_origin, Ea = None, None - if 'rainbow' in self._runtype or 'c51' in self._runtype: - _, loss, prob, targ, states, allQ = self._sess.run(self._train_op) - if self.training_steps % 100000 == 0: - print (np.sum(prob, -1)) - print (loss[0]) - tmp = [targ[0], prob[0], allQ[0]] - if pv is not None: - tmp.extend(pv) - tmp.extend(self.v_sup_) - #print (self.training_steps, states.shape, prob.shape, pv.shape, pa.shape, allQ.shape) - if (self.summary_writer is not None and - self.training_steps > 0 and - self.training_steps % self.summary_writing_frequency == 0): - summary = self._sess.run(self._merged_summaries) - self.summary_writer.add_summary(summary, self.training_steps) - - if self.training_steps % self.target_update_period == 0: - self._sess.run(self._sync_qt_ops) - - self.training_steps += 1 - - - def _select_action(self): - """Select an action from the set of available actions. - - Chooses an action randomly with probability self._calculate_epsilon(), and - otherwise acts greedily according to the current Q-value estimates. - - Returns: - int, the selected action. - """ - if self.eval_mode: - epsilon = self.epsilon_eval - else: - epsilon = self.epsilon_fn( - self.epsilon_decay_period, - self.training_steps, - self.min_replay_history, - self.epsilon_train) - # Choose the action with highest Q-value at the current state. - #q_argmax, p = self._sess.run([self._q_argmax, self._net_outputs.probabilities], {self.state_ph: self.state}) - if self.testing: - #print (self.subcontrol) - if 'iqn' not in self._runtype: - q_argmax, v_ = self._sess.run([self._q_argmax, self._net_outputs.probabilities], {self.state_ph: self.state}) - v_ = v_[0, q_argmax, :] - self.vis['v'].append(v_) - else: - q_argmax = self._sess.run(self._q_argmax, {self.state_ph: self.state}) - else: - q_argmax = self._sess.run(self._q_argmax, {self.state_ph: self.state}) - if random.random() <= epsilon: - # Choose a random action with probability epsilon. - return random.randint(0, self.num_actions - 1) - else: - return q_argmax - - - def _build_target_distribution(self, q_support=None): - """Builds the C51 target distribution as per Bellemare et al. (2017). - - First, we compute the support of the Bellman target, r + gamma Z'. Where Z' - is the support of the next state distribution: - - * Evenly spaced in [-vmax, vmax] if the current state is nonterminal; - * 0 otherwise (duplicated num_atoms times). - - Second, we compute the next-state probabilities, corresponding to the action - with highest expected value. - - Finally we project the Bellman target (support + probabilities) onto the - original support. - - Returns: - target_distribution: tf.tensor, the target distribution from the replay. - """ - - if q_support is not None: - _support = q_support - batch_size = self._replay.batch_size - - # size of rewards: batch_size x 1 - rewards = self._replay.rewards[:, None] - - # size of tiled_support: batch_size x num_atoms - #tiled_support = tf.tile(_support, [batch_size]) - #tiled_support = tf.reshape(tiled_support, [batch_size, self._num_atoms]) - tiled_support = _support - - # size of target_support: batch_size x num_atoms - - is_terminal_multiplier = 1. - tf.cast(self._replay.terminals, tf.float32) - # Incorporate terminal state to discount factor. - # size of gamma_with_terminal: batch_size x 1 - gamma_with_terminal = self.cumulative_gamma * is_terminal_multiplier - gamma_with_terminal = gamma_with_terminal[:, None] - - target_support = rewards + gamma_with_terminal * tiled_support - - # size of next_qt_argmax: 1 x batch_size - next_qt_argmax = tf.argmax( - self._replay_next_target_net_outputs.q_values, axis=1)[:, None] - batch_indices = tf.range(tf.to_int64(batch_size))[:, None] - # size of next_qt_argmax: batch_size x 2 - batch_indexed_next_qt_argmax = tf.concat( - [batch_indices, next_qt_argmax], axis=1) - - # size of next_probabilities: batch_size x num_atoms - next_probabilities = tf.gather_nd( - self._replay_next_target_net_outputs.probabilities, - batch_indexed_next_qt_argmax) - - return project_distribution_1(target_support, next_probabilities, - _support) - else: - _support = self._support - batch_size = self._replay.batch_size - - # size of rewards: batch_size x 1 - rewards = self._replay.rewards[:, None] - - # size of tiled_support: batch_size x num_atoms - tiled_support = tf.tile(_support, [batch_size]) - tiled_support = tf.reshape(tiled_support, [batch_size, self._num_atoms]) - - # size of target_support: batch_size x num_atoms - - is_terminal_multiplier = 1. - tf.cast(self._replay.terminals, tf.float32) - # Incorporate terminal state to discount factor. - # size of gamma_with_terminal: batch_size x 1 - gamma_with_terminal = self.cumulative_gamma * is_terminal_multiplier - gamma_with_terminal = gamma_with_terminal[:, None] - - target_support = rewards + gamma_with_terminal * tiled_support - - # size of next_qt_argmax: 1 x batch_size - next_qt_argmax = tf.argmax( - self._replay_next_target_net_outputs.q_values, axis=1)[:, None] - batch_indices = tf.range(tf.to_int64(batch_size))[:, None] - # size of next_qt_argmax: batch_size x 2 - batch_indexed_next_qt_argmax = tf.concat( - [batch_indices, next_qt_argmax], axis=1) - - # size of next_probabilities: batch_size x num_atoms - next_probabilities = tf.gather_nd( - self._replay_next_target_net_outputs.probabilities, - batch_indexed_next_qt_argmax) - - return project_distribution(target_support, next_probabilities, - _support) - - - def _build_train_op(self): - """Builds a training op. - - Returns: - train_op: An op performing one step of training from replay data. - """ - target_distribution = tf.stop_gradient(self._build_target_distribution(self._replay_net_outputs.q_support)) - - # size of indices: batch_size x 1. - indices = tf.range(tf.shape(self._replay_net_outputs.probabilities)[0])[:, None] - # size of reshaped_actions: batch_size x 2. - print ("replay_action.shape, ", self._replay.actions.shape) - reshaped_actions = tf.concat([indices, self._replay.actions[:, None]], 1) - # For each element of the batch, fetch the logits for its selected action. - chosen_action_probabilities = tf.gather_nd(self._replay_net_outputs.probabilities, - reshaped_actions) - print ("----------------------------------------------------------") - print (self._replay_net_outputs.probabilities.shape, reshaped_actions.shape, chosen_action_probabilities.shape) - all_action_probabilities = self._replay_net_outputs.probabilities - cross_entropy = -1 * target_distribution * tf.log(chosen_action_probabilities + 1e-8) - loss = tf.reduce_sum(cross_entropy, axis=-1) - original_loss = loss - #loss = tf.reduce_mean(loss, axis=-1) - print (">>>>>>>>>>>>>>loss-prob:", loss.shape) - print (self._replay_net_outputs.a) - - if self._replay_net_outputs.a is not None: - chosen_pa = tf.gather_nd(self._replay_net_outputs.a, reshaped_actions) - pa = self._replay_net_outputs.a - - ''' - # size of indices: batch_size x 1. - indices = tf.range(tf.shape(self._replay_net_outputs.logits)[0])[:, None] - # size of reshaped_actions: batch_size x 2. - reshaped_actions = tf.concat([indices, self._replay.actions[:, None]], 1) - # For each element of the batch, fetch the logits for its selected action. - chosen_action_logits = tf.gather_nd(self._replay_net_outputs.logits, - reshaped_actions) - - loss1 = tf.nn.softmax_cross_entropy_with_logits( - labels=target_distribution, - logits=chosen_action_logits) - print (">>>>>>>>>>>>>>loss-logits:", loss1.shape) - ''' - - if self._replay_scheme == 'prioritized': - # The original prioritized experience replay uses a linear exponent - # schedule 0.4 -> 1.0. Comparing the schedule to a fixed exponent of 0.5 - # on 5 games (Asterix, Pong, Q*Bert, Seaquest, Space Invaders) suggested - # a fixed exponent actually performs better, except on Pong. - probs = self._replay.transition['sampling_probabilities'] - loss_weights = 1.0 / tf.sqrt(probs + 1e-10) - loss_weights /= tf.reduce_max(loss_weights) - - # Rainbow and prioritized replay are parametrized by an exponent alpha, - # but in both cases it is set to 0.5 - for simplicity's sake we leave it - # as is here, using the more direct tf.sqrt(). Taking the square root - # "makes sense", as we are dealing with a squared loss. - # Add a small nonzero value to the loss to avoid 0 priority items. While - # technically this may be okay, setting all items to 0 priority will cause - # troubles, and also result in 1.0 / 0.0 = NaN correction terms. - update_priorities_op = self._replay.tf_set_priority( - self._replay.indices, tf.sqrt(loss + 1e-10)) - - # Weight the loss by the inverse priorities. - loss = loss_weights * loss - else: - update_priorities_op = tf.no_op() - - with tf.control_dependencies([update_priorities_op]): - if self.summary_writer is not None: - with tf.variable_scope('Losses'): - tf.summary.scalar('CrossEntropyLoss', tf.reduce_mean(loss)) - # Schaul et al. reports a slightly different rule, where 1/N is also - # exponentiated by beta. Not doing so seems more reasonable, and did not - # impact performance in our experiments. - var = tf.trainable_variables() - print ("all trainable var ----------------------", var) - return self.optimizer.minimize(tf.reduce_mean(loss)), loss, chosen_action_probabilities,\ - target_distribution, self._replay.states, all_action_probabilities - - def _store_transition(self, - last_observation, - action, - reward, - is_terminal, - priority=None): - """Stores a transition when in training mode. - - Executes a tf session and executes replay buffer ops in order to store the - following tuple in the replay buffer (last_observation, action, reward, - is_terminal, priority). - - Args: - last_observation: Last observation, type determined via observation_type - parameter in the replay_memory constructor. - action: An integer, the action taken. - reward: A float, the reward. - is_terminal: Boolean indicating if the current state is a terminal state. - priority: Float. Priority of sampling the transition. If None, the default - priority will be used. If replay scheme is uniform, the default priority - is 1. If the replay scheme is prioritized, the default priority is the - maximum ever seen [Schaul et al., 2015]. - """ - if priority is None: - if self._replay_scheme == 'uniform': - priority = 1. - else: - priority = self._replay.memory.sum_tree.max_recorded_priority - - if not self.eval_mode: - self._replay.add(last_observation, action, reward, is_terminal, priority) - - -def project_distribution(supports, weights, target_support, - validate_args=False): - """Projects a batch of (support, weights) onto target_support. - - Based on equation (7) in (Bellemare et al., 2017): - https://arxiv.org/abs/1707.06887 - In the rest of the comments we will refer to this equation simply as Eq7. - - This code is not easy to digest, so we will use a running example to clarify - what is going on, with the following sample inputs: - - * supports = [[0, 2, 4, 6, 8], - [1, 3, 4, 5, 6]] - * weights = [[0.1, 0.6, 0.1, 0.1, 0.1], - [0.1, 0.2, 0.5, 0.1, 0.1]] - * target_support = [4, 5, 6, 7, 8] - - In the code below, comments preceded with 'Ex:' will be referencing the above - values. - - Args: - supports: Tensor of shape (batch_size, num_dims) defining supports for the - distribution. - weights: Tensor of shape (batch_size, num_dims) defining weights on the - original support points. Although for the CategoricalDQN agent these - weights are probabilities, it is not required that they are. - target_support: Tensor of shape (num_dims) defining support of the projected - distribution. The values must be monotonically increasing. Vmin and Vmax - will be inferred from the first and last elements of this tensor, - respectively. The values in this tensor must be equally spaced. - validate_args: Whether we will verify the contents of the - target_support parameter. - - Returns: - A Tensor of shape (batch_size, num_dims) with the projection of a batch of - (support, weights) onto target_support. - - Raises: - ValueError: If target_support has no dimensions, or if shapes of supports, - weights, and target_support are incompatible. - """ - target_support_deltas = target_support[1:] - target_support[:-1] - # delta_z = `\Delta z` in Eq7. - delta_z = target_support_deltas[0] - validate_deps = [] - supports.shape.assert_is_compatible_with(weights.shape) - supports[0].shape.assert_is_compatible_with(target_support.shape) - target_support.shape.assert_has_rank(1) - if validate_args: - # Assert that supports and weights have the same shapes. - validate_deps.append( - tf.Assert( - tf.reduce_all(tf.equal(tf.shape(supports), tf.shape(weights))), - [supports, weights])) - # Assert that elements of supports and target_support have the same shape. - validate_deps.append( - tf.Assert( - tf.reduce_all( - tf.equal(tf.shape(supports)[1], tf.shape(target_support))), - [supports, target_support])) - # Assert that target_support has a single dimension. - validate_deps.append( - tf.Assert( - tf.equal(tf.size(tf.shape(target_support)), 1), [target_support])) - # Assert that the target_support is monotonically increasing. - validate_deps.append( - tf.Assert(tf.reduce_all(target_support_deltas > 0), [target_support])) - # Assert that the values in target_support are equally spaced. - validate_deps.append( - tf.Assert( - tf.reduce_all(tf.equal(target_support_deltas, delta_z)), - [target_support])) - - with tf.control_dependencies(validate_deps): - # Ex: `v_min, v_max = 4, 8`. - v_min, v_max = target_support[0], target_support[-1] - # Ex: `batch_size = 2`. - batch_size = tf.shape(supports)[0] - # `N` in Eq7. - # Ex: `num_dims = 5`. - num_dims = tf.shape(target_support)[0] - # clipped_support = `[\hat{T}_{z_j}]^{V_max}_{V_min}` in Eq7. - # Ex: `clipped_support = [[[ 4. 4. 4. 6. 8.]] - # [[ 4. 4. 4. 5. 6.]]]`. - clipped_support = tf.clip_by_value(supports, v_min, v_max)[:, None, :] - # Ex: `tiled_support = [[[[ 4. 4. 4. 6. 8.] - # [ 4. 4. 4. 6. 8.] - # [ 4. 4. 4. 6. 8.] - # [ 4. 4. 4. 6. 8.] - # [ 4. 4. 4. 6. 8.]] - # [[ 4. 4. 4. 5. 6.] - # [ 4. 4. 4. 5. 6.] - # [ 4. 4. 4. 5. 6.] - # [ 4. 4. 4. 5. 6.] - # [ 4. 4. 4. 5. 6.]]]]`. - tiled_support = tf.tile([clipped_support], [1, 1, num_dims, 1]) - # Ex: `reshaped_target_support = [[[ 4.] - # [ 5.] - # [ 6.] - # [ 7.] - # [ 8.]] - # [[ 4.] - # [ 5.] - # [ 6.] - # [ 7.] - # [ 8.]]]`. - reshaped_target_support = tf.tile(target_support[:, None], [batch_size, 1]) - reshaped_target_support = tf.reshape(reshaped_target_support, - [batch_size, num_dims, 1]) - # numerator = `|clipped_support - z_i|` in Eq7. - # Ex: `numerator = [[[[ 0. 0. 0. 2. 4.] - # [ 1. 1. 1. 1. 3.] - # [ 2. 2. 2. 0. 2.] - # [ 3. 3. 3. 1. 1.] - # [ 4. 4. 4. 2. 0.]] - # [[ 0. 0. 0. 1. 2.] - # [ 1. 1. 1. 0. 1.] - # [ 2. 2. 2. 1. 0.] - # [ 3. 3. 3. 2. 1.] - # [ 4. 4. 4. 3. 2.]]]]`. - numerator = tf.abs(tiled_support - reshaped_target_support) - quotient = 1 - (numerator / delta_z) - # clipped_quotient = `[1 - numerator / (\Delta z)]_0^1` in Eq7. - # Ex: `clipped_quotient = [[[[ 1. 1. 1. 0. 0.] - # [ 0. 0. 0. 0. 0.] - # [ 0. 0. 0. 1. 0.] - # [ 0. 0. 0. 0. 0.] - # [ 0. 0. 0. 0. 1.]] - # [[ 1. 1. 1. 0. 0.] - # [ 0. 0. 0. 1. 0.] - # [ 0. 0. 0. 0. 1.] - # [ 0. 0. 0. 0. 0.] - # [ 0. 0. 0. 0. 0.]]]]`. - clipped_quotient = tf.clip_by_value(quotient, 0, 1) - # Ex: `weights = [[ 0.1 0.6 0.1 0.1 0.1] - # [ 0.1 0.2 0.5 0.1 0.1]]`. - weights = weights[:, None, :] - # inner_prod = `\sum_{j=0}^{N-1} clipped_quotient * p_j(x', \pi(x'))` - # in Eq7. - # Ex: `inner_prod = [[[[ 0.1 0.6 0.1 0. 0. ] - # [ 0. 0. 0. 0. 0. ] - # [ 0. 0. 0. 0.1 0. ] - # [ 0. 0. 0. 0. 0. ] - # [ 0. 0. 0. 0. 0.1]] - # [[ 0.1 0.2 0.5 0. 0. ] - # [ 0. 0. 0. 0.1 0. ] - # [ 0. 0. 0. 0. 0.1] - # [ 0. 0. 0. 0. 0. ] - # [ 0. 0. 0. 0. 0. ]]]]`. - inner_prod = clipped_quotient * weights - # Ex: `projection = [[ 0.8 0.0 0.1 0.0 0.1] - # [ 0.8 0.1 0.1 0.0 0.0]]`. - projection = tf.reduce_sum(inner_prod, 3) - projection = tf.reshape(projection, [batch_size, num_dims]) - return projection - - -def project_distribution_1(supports, weights, target_support, - validate_args=False): - """Projects a batch of (support, weights) onto target_support. - - Based on equation (7) in (Bellemare et al., 2017): - https://arxiv.org/abs/1707.06887 - In the rest of the comments we will refer to this equation simply as Eq7. - - This code is not easy to digest, so we will use a running example to clarify - what is going on, with the following sample inputs: - - * supports = [[0, 2, 4, 6, 8], - [1, 3, 4, 5, 6]] - * weights = [[0.1, 0.6, 0.1, 0.1, 0.1], - [0.1, 0.2, 0.5, 0.1, 0.1]] - * target_support = [4, 5, 6, 7, 8] - - In the code below, comments preceded with 'Ex:' will be referencing the above - values. - - Args: - supports: Tensor of shape (batch_size, num_dims) defining supports for the - distribution. - weights: Tensor of shape (batch_size, num_dims) defining weights on the - original support points. Although for the CategoricalDQN agent these - weights are probabilities, it is not required that they are. - target_support: Tensor of shape (num_dims) defining support of the projected - distribution. The values must be monotonically increasing. Vmin and Vmax - will be inferred from the first and last elements of this tensor, - respectively. The values in this tensor must be equally spaced. - validate_args: Whether we will verify the contents of the - target_support parameter. - - Returns: - A Tensor of shape (batch_size, num_dims) with the projection of a batch of - (support, weights) onto target_support. - - Raises: - ValueError: If target_support has no dimensions, or if shapes of supports, - weights, and target_support are incompatible. - """ - target_support_deltas = target_support[0,1:] - target_support[0,:-1] - # delta_z = `\Delta z` in Eq7. - delta_z = target_support_deltas[0] - validate_deps = [] - supports.shape.assert_is_compatible_with(weights.shape) - supports[0].shape.assert_is_compatible_with(target_support[0].shape) - target_support.shape.assert_has_rank(2) - if validate_args: - # Assert that supports and weights have the same shapes. - validate_deps.append( - tf.Assert( - tf.reduce_all(tf.equal(tf.shape(supports), tf.shape(weights))), - [supports, weights])) - # Assert that elements of supports and target_support have the same shape. - validate_deps.append( - tf.Assert( - tf.reduce_all( - tf.equal(tf.shape(supports)[1], tf.shape(target_support[0]))), - [supports, target_support[0]])) - # Assert that target_support has a single dimension. - validate_deps.append( - tf.Assert( - tf.equal(tf.size(tf.shape(target_support[0])), 1), [target_support[0]])) - # Assert that the target_support is monotonically increasing. - validate_deps.append( - tf.Assert(tf.reduce_all(target_support_deltas > 0), [target_support[0]])) - # Assert that the values in target_support are equally spaced. - validate_deps.append( - tf.Assert( - tf.reduce_all(tf.equal(target_support_deltas, delta_z)), - [target_support[0]])) - - with tf.control_dependencies(validate_deps): - # Ex: `v_min, v_max = 4, 8`. - v_min, v_max = target_support[:,0], target_support[:,-1] - # Ex: `batch_size = 2`. - batch_size = tf.shape(supports)[0] - # `N` in Eq7. - # Ex: `num_dims = 5`. - num_dims = tf.shape(target_support[0])[0] - # clipped_support = `[\hat{T}_{z_j}]^{V_max}_{V_min}` in Eq7. - # Ex: `clipped_support = [[[ 4. 4. 4. 6. 8.]] - # [[ 4. 4. 4. 5. 6.]]]`. - #TODO - #clipped_support = tf.map_fn(fn=lambda inp: tf.clip_by_value(inp[0], inp[1], inp[2]), elems=[supports, v_min, v_max], dtype=tf.float32) - v_min1 = tf.tile(tf.reshape(v_min, [-1, 1]), [1, num_dims]) - v_max1 = tf.tile(tf.reshape(v_max, [-1, 1]), [1, num_dims]) - clipped_support = tf.minimum(tf.maximum(supports, v_min1), v_max1) - #clipped_support = tf.clip_by_value(supports, v_min[0], v_max[0]) - print ('----', clipped_support.shape) - clipped_support = clipped_support[:, None, :] - print ('----', clipped_support.shape) - # Ex: `tiled_support = [[[[ 4. 4. 4. 6. 8.] - # [ 4. 4. 4. 6. 8.] - # [ 4. 4. 4. 6. 8.] - # [ 4. 4. 4. 6. 8.] - # [ 4. 4. 4. 6. 8.]] - # [[ 4. 4. 4. 5. 6.] - # [ 4. 4. 4. 5. 6.] - # [ 4. 4. 4. 5. 6.] - # [ 4. 4. 4. 5. 6.] - # [ 4. 4. 4. 5. 6.]]]]`. - tiled_support = tf.tile([clipped_support], [1, 1, num_dims, 1]) - # Ex: `reshaped_target_support = [[[ 4.] - # [ 5.] - # [ 6.] - # [ 7.] - # [ 8.]] - # [[ 4.] - # [ 5.] - # [ 6.] - # [ 7.] - # [ 8.]]]`. - reshaped_target_support = target_support #tf.tile(target_support[:, None], [batch_size, 1]) - reshaped_target_support = tf.reshape(reshaped_target_support, - [batch_size, num_dims, 1]) - # numerator = `|clipped_support - z_i|` in Eq7. - # Ex: `numerator = [[[[ 0. 0. 0. 2. 4.] - # [ 1. 1. 1. 1. 3.] - # [ 2. 2. 2. 0. 2.] - # [ 3. 3. 3. 1. 1.] - # [ 4. 4. 4. 2. 0.]] - # [[ 0. 0. 0. 1. 2.] - # [ 1. 1. 1. 0. 1.] - # [ 2. 2. 2. 1. 0.] - # [ 3. 3. 3. 2. 1.] - # [ 4. 4. 4. 3. 2.]]]]`. - numerator = tf.abs(tiled_support - reshaped_target_support) - quotient = 1 - (numerator / delta_z) - # clipped_quotient = `[1 - numerator / (\Delta z)]_0^1` in Eq7. - # Ex: `clipped_quotient = [[[[ 1. 1. 1. 0. 0.] - # [ 0. 0. 0. 0. 0.] - # [ 0. 0. 0. 1. 0.] - # [ 0. 0. 0. 0. 0.] - # [ 0. 0. 0. 0. 1.]] - # [[ 1. 1. 1. 0. 0.] - # [ 0. 0. 0. 1. 0.] - # [ 0. 0. 0. 0. 1.] - # [ 0. 0. 0. 0. 0.] - # [ 0. 0. 0. 0. 0.]]]]`. - clipped_quotient = tf.clip_by_value(quotient, 0, 1) - # Ex: `weights = [[ 0.1 0.6 0.1 0.1 0.1] - # [ 0.1 0.2 0.5 0.1 0.1]]`. - weights = weights[:, None, :] - # inner_prod = `\sum_{j=0}^{N-1} clipped_quotient * p_j(x', \pi(x'))` - # in Eq7. - # Ex: `inner_prod = [[[[ 0.1 0.6 0.1 0. 0. ] - # [ 0. 0. 0. 0. 0. ] - # [ 0. 0. 0. 0.1 0. ] - # [ 0. 0. 0. 0. 0. ] - # [ 0. 0. 0. 0. 0.1]] - # [[ 0.1 0.2 0.5 0. 0. ] - # [ 0. 0. 0. 0.1 0. ] - # [ 0. 0. 0. 0. 0.1] - # [ 0. 0. 0. 0. 0. ] - # [ 0. 0. 0. 0. 0. ]]]]`. - inner_prod = clipped_quotient * weights - # Ex: `projection = [[ 0.8 0.0 0.1 0.0 0.1] - # [ 0.8 0.1 0.1 0.0 0.0]]`. - projection = tf.reduce_sum(inner_prod, 3) - projection = tf.reshape(projection, [batch_size, num_dims]) - return projection +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Compact implementation of a simplified Rainbow agent. + +Specifically, we implement the following components from Rainbow: + + * n-step updates; + * prioritized replay; and + * distributional RL. + +These three components were found to significantly impact the performance of +the Atari game-playing agent. + +Furthermore, our implementation does away with some minor hyperparameter +choices. Specifically, we + + * keep the beta exponent fixed at beta=0.5, rather than increase it linearly; + * remove the alpha parameter, which was set to alpha=0.5 throughout the paper. + +Details in "Rainbow: Combining Improvements in Deep Reinforcement Learning" by +Hessel et al. (2018). +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import csv, json, pickle +import multiprocessing + + + +from dopamine.agents.dqn import dqn_agent +from dopamine.discrete_domains import atari_lib +from dopamine.replay_memory import prioritized_replay_buffer +import tensorflow as tf +import random +import numpy as np + +import gin.tf + +slim = tf.contrib.slim + + +@gin.configurable +class RainbowAgent(dqn_agent.DQNAgent): + """A compact implementation of a simplified Rainbow agent.""" + + def __init__(self, + sess, + num_actions, + observation_shape=dqn_agent.NATURE_DQN_OBSERVATION_SHAPE, + observation_dtype=dqn_agent.NATURE_DQN_DTYPE, + stack_size=dqn_agent.NATURE_DQN_STACK_SIZE, + network=atari_lib.rainbow_network, + runtype='run', + game=None, + num_atoms=51, + vmax=10., + gamma=0.99, + update_horizon=1, + min_replay_history=20000, + update_period=4, + target_update_period=8000, + epsilon_fn=dqn_agent.linearly_decaying_epsilon, + epsilon_train=0.01, + epsilon_eval=0.001, + epsilon_decay_period=250000, + replay_scheme='prioritized', + tf_device='/cpu:*', + use_staging=True, + optimizer=tf.train.AdamOptimizer( + learning_rate=0.00025, epsilon=0.0003125), + summary_writer=None, + summary_writing_frequency=500): + """Initializes the agent and constructs the components of its graph. + + Args: + sess: `tf.Session`, for executing ops. + num_actions: int, number of actions the agent can take at any state. + observation_shape: tuple of ints or an int. If single int, the observation + is assumed to be a 2D square. + observation_dtype: tf.DType, specifies the type of the observations. Note + that if your inputs are continuous, you should set this to tf.float32. + stack_size: int, number of frames to use in state stack. + network: function expecting three parameters: + (num_actions, network_type, state). This function will return the + network_type object containing the tensors output by the network. + See dopamine.discrete_domains.atari_lib.rainbow_network as + an example. + num_atoms: int, the number of buckets of the value function distribution. + vmax: float, the value distribution support is [-vmax, vmax]. + gamma: float, discount factor with the usual RL meaning. + update_horizon: int, horizon at which updates are performed, the 'n' in + n-step update. + min_replay_history: int, number of transitions that should be experienced + before the agent begins training its value function. + update_period: int, period between DQN updates. + target_update_period: int, update period for the target network. + epsilon_fn: function expecting 4 parameters: + (decay_period, step, warmup_steps, epsilon). This function should return + the epsilon value used for exploration during training. + epsilon_train: float, the value to which the agent's epsilon is eventually + decayed during training. + epsilon_eval: float, epsilon used when evaluating the agent. + epsilon_decay_period: int, length of the epsilon decay schedule. + replay_scheme: str, 'prioritized' or 'uniform', the sampling scheme of the + replay memory. + tf_device: str, Tensorflow device on which the agent's graph is executed. + use_staging: bool, when True use a staging area to prefetch the next + training batch, speeding training up by about 30%. + optimizer: `tf.train.Optimizer`, for training the value function. + summary_writer: SummaryWriter object for outputting training statistics. + Summary writing disabled if set to None. + summary_writing_frequency: int, frequency with which summaries will be + written. Lower values will result in slower training. + """ + self.ACTION_MEANING = { + 0: "NOOP", + 1: "FIRE", + 2: "UP", + 3: "RIGHT", + 4: "LEFT", + 5: "DOWN", + 6: "UPRIGHT", + 7: "UPLEFT", + 8: "DOWNRIGHT", + 9: "DOWNLEFT", + 10: "UPFIRE", + 11: "RIGHTFIRE", + 12: "LEFTFIRE", + 13: "DOWNFIRE", + 14: "UPRIGHTFIRE", + 15: "UPLEFTFIRE", + 16: "DOWNRIGHTFIRE", + 17: "DOWNLEFTFIRE", + } + # We need this because some tools convert round floats into ints. + self.testing = False + vmax = float(vmax) + self.N = 1 + self.index = None + self.big_z = None + self.big_a = None + self.big_qv = None + self.unique_num = None + self.v_sup_tensor = None #[tf.constant(vv, dtype=tf.float32) for vv in v_sup_] + self.M = self.sp_a = self.sortsp_a = None + if 'rainbow' in runtype: + num_atoms = int(runtype.split('-')[0][7:]) + num_atoms_sub = self._num_atoms_sub = num_atoms + elif 'c51' in runtype: + num_atoms_sub = self._num_atoms_sub = 51 + else: + num_atoms_sub = num_atoms + self._num_atoms = num_atoms + self._runtype = runtype + self._support = tf.linspace(-vmax, vmax, num_atoms) + self.v_sup_ = np.array(np.linspace(-vmax, vmax, num_atoms)) + self.v_support = tf.linspace(-vmax, vmax, num_atoms_sub) + self.a_support = tf.linspace(-vmax, vmax, num_atoms_sub) #V + k * A + self._game = game + self._klfactor = 0.0 + self._entfactor = 0 + self._ckfactor = 0 + self._num_actions = num_actions + print ("NUM_ATOMS:", num_atoms) + self.q_sup = [] + for i in range(self.N): + self.q_sup.append(np.reshape(np.linspace(-10, 10, num_atoms), (1, num_atoms))) + print ("RUNTYPE:", runtype) + print ("VMAX:", vmax) + print (">>>>>>>>", multiprocessing.cpu_count()) + self.k = 1.0 + print ("-------kkkkk =", self.k) + self.v_support = tf.reshape(self.v_support, (1, 1, num_atoms_sub)) + self.a_support = tf.reshape(self.a_support, (1, 1, num_atoms_sub)) + + self._replay_scheme = replay_scheme + self._filename = './fout-visual/visual-%s_%s.pkl' % (self._game, self._runtype) + self._filename_vis = './vis-%s_%s.pkl' % (self._game, self._runtype) + print ('==========================', self._filename) + self.dict = {"training_step": [], + "state": [], + "prob": [], + "loss": [], + "klloss": [] + } + # TODO(b/110897128): Make agent optimizer attribute private. + self.vis = {"sup": [], "v": [], "s": [], "a": [], "supF": [], "F": [], "r": [], "state_input":[], "gradient":[], + "fraction": [], 'values': []} + self.vis['sup'] = self.q_sup #self.v_sup_ + self.vis['supF'] = self.v_sup_ + self.optimizer = optimizer + + dqn_agent.DQNAgent.__init__( + self, + sess=sess, + num_actions=num_actions, + observation_shape=observation_shape, + observation_dtype=observation_dtype, + stack_size=stack_size, + network=network, + gamma=gamma, + update_horizon=update_horizon, + min_replay_history=min_replay_history, + update_period=update_period, + target_update_period=target_update_period, + epsilon_fn=epsilon_fn, + epsilon_train=epsilon_train, + epsilon_eval=epsilon_eval, + epsilon_decay_period=epsilon_decay_period, + tf_device=tf_device, + use_staging=use_staging, + optimizer=self.optimizer, + summary_writer=summary_writer, + summary_writing_frequency=summary_writing_frequency) + print (self._sess.run(self._support)) + #exit(0) + + def _get_network_type(self): + """Returns the type of the outputs of a value distribution network. + + Returns: + net_type: _network_type object defining the outputs of the network. + """ + return collections.namedtuple('c51_network', + ['q_values', 'logits', 'probabilities', 'v_', 'a', 'q_v', 'a_origin', 'Ea', 'q_support', 'a_support', 'q_values_sub', 'state_input']) + + def _build_networks(self): + """Builds the Q-value network computations needed for acting and training. + + These are: + self.online_convnet: For computing the current state's Q-values. + self.target_convnet: For computing the next state's target Q-values. + self._net_outputs: The actual Q-values. + self._q_argmax: The action maximizing the current state's Q-values. + self._replay_net_outputs: The replayed states' Q-values. + self._replay_next_target_net_outputs: The replayed next states' target + Q-values (see Mnih et al., 2015 for details). + """ + # Calling online_convnet will generate a new graph as defined in + # self._get_network_template using whatever input is passed, but will always + # share the same weights. + self.online_convnet = tf.make_template('Online', self._network_template) + self.target_convnet = tf.make_template('Target', self._network_template) + self._net_outputs = self.online_convnet(self.state_ph) + # TODO(bellemare): Ties should be broken. They are unlikely to happen when + # using a deep network, but may affect performance with a linear + # approximation scheme. + self._q_argmax = tf.argmax(self._net_outputs.q_values, axis=1)[0] + self.gradient = [] + for q_sub in self._net_outputs.q_values_sub: + print (self._q_argmax.shape) + chosen_action_q_sub = q_sub[0][self._q_argmax] ##Z_1(a), Z_2(a) + print ("chosen_action_q_sub:", chosen_action_q_sub.shape) + self.gradient.append(tf.gradients(chosen_action_q_sub, [self._net_outputs.state_input])) + + self._replay_net_outputs = self.online_convnet(self._replay.states) + self._replay_next_target_net_outputs = self.target_convnet( + self._replay.next_states) + + def _network_template(self, state): + """Builds a convolutional network that outputs Q-value distributions. + + Args: + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + return self.network(self.num_actions, self._num_atoms, self._num_atoms_sub, self._support, + self._get_network_type(), state, self._runtype, + self.v_support, self.a_support, self.big_z, self.big_a, self.big_qv, self.N, self.index, self.M, self.sp_a, self.unique_num, self.sortsp_a, self.v_sup_tensor) + #self.v_support, self.a_support, self.big_z, self.idx_matrix_add_onehot, self.idx_matrix_minus_onehot) + + def _build_replay_buffer(self, use_staging): + """Creates the replay buffer used by the agent. + + Args: + use_staging: bool, if True, uses a staging area to prefetch data for + faster training. + + Returns: + A `WrappedPrioritizedReplayBuffer` object. + + Raises: + ValueError: if given an invalid replay scheme. + """ + if self._replay_scheme not in ['uniform', 'prioritized']: + raise ValueError('Invalid replay scheme: {}'.format(self._replay_scheme)) + return prioritized_replay_buffer.WrappedPrioritizedReplayBuffer( + observation_shape=self.observation_shape, + stack_size=self.stack_size, + use_staging=use_staging, + update_horizon=self.update_horizon, + gamma=self.gamma) + + def step(self, reward, observation): + """Records the most recent transition and returns the agent's next action. + + We store the observation of the last time step since we want to store it + with the reward. + + Args: + reward: float, the reward received from the agent's most recent action. + observation: numpy array, the most recent observation. + + Returns: + int, the selected action. + """ + self._last_observation = self._observation + self._record_observation(observation) + + if not self.eval_mode: + self._store_transition(self._last_observation, self.action, reward, False) + self._train_step() + + self.action = self._select_action() + return self.action + + def _train_step(self): + """Runs a single training step. + + Runs a training op if both: + (1) A minimum number of frames have been added to the replay buffer. + (2) `training_steps` is a multiple of `update_period`. + + Also, syncs weights from online to target network if training steps is a + multiple of target update period. + """ + # Run a train op at the rate of self.update_period if enough training steps + # have been run. This matches the Nature DQN behaviour. + if self._replay.memory.add_count > self.min_replay_history: + if self.training_steps % self.update_period == 0: + if 'iqn' in self._runtype: + self._sess.run(self._train_op) + else: + q_sup = None; a_sup = None + pv, pa = None, None + a_origin, Ea = None, None + if 'rainbow' in self._runtype or 'c51' in self._runtype: + _, loss, prob, targ, states, allQ = self._sess.run(self._train_op) + if self.training_steps % 100000 == 0: + print (np.sum(prob, -1)) + print (loss[0]) + tmp = [targ[0], prob[0], allQ[0]] + if pv is not None: + tmp.extend(pv) + tmp.extend(self.v_sup_) + #print (self.training_steps, states.shape, prob.shape, pv.shape, pa.shape, allQ.shape) + if (self.summary_writer is not None and + self.training_steps > 0 and + self.training_steps % self.summary_writing_frequency == 0): + summary = self._sess.run(self._merged_summaries) + self.summary_writer.add_summary(summary, self.training_steps) + + if self.training_steps % self.target_update_period == 0: + self._sess.run(self._sync_qt_ops) + + self.training_steps += 1 + + + def _select_action(self): + """Select an action from the set of available actions. + + Chooses an action randomly with probability self._calculate_epsilon(), and + otherwise acts greedily according to the current Q-value estimates. + + Returns: + int, the selected action. + """ + if self.eval_mode: + epsilon = self.epsilon_eval + else: + epsilon = self.epsilon_fn( + self.epsilon_decay_period, + self.training_steps, + self.min_replay_history, + self.epsilon_train) + # Choose the action with highest Q-value at the current state. + #q_argmax, p = self._sess.run([self._q_argmax, self._net_outputs.probabilities], {self.state_ph: self.state}) + if self.testing: + #print (self.subcontrol) + if 'iqn' not in self._runtype: + q_argmax, v_ = self._sess.run([self._q_argmax, self._net_outputs.probabilities], {self.state_ph: self.state}) + v_ = v_[0, q_argmax, :] + self.vis['v'].append(v_) + else: + q_argmax = self._sess.run(self._q_argmax, {self.state_ph: self.state}) + else: + q_argmax = self._sess.run(self._q_argmax, {self.state_ph: self.state}) + if random.random() <= epsilon: + # Choose a random action with probability epsilon. + return random.randint(0, self.num_actions - 1) + else: + return q_argmax + + + def _build_target_distribution(self, q_support=None): + """Builds the C51 target distribution as per Bellemare et al. (2017). + + First, we compute the support of the Bellman target, r + gamma Z'. Where Z' + is the support of the next state distribution: + + * Evenly spaced in [-vmax, vmax] if the current state is nonterminal; + * 0 otherwise (duplicated num_atoms times). + + Second, we compute the next-state probabilities, corresponding to the action + with highest expected value. + + Finally we project the Bellman target (support + probabilities) onto the + original support. + + Returns: + target_distribution: tf.tensor, the target distribution from the replay. + """ + + if q_support is not None: + _support = q_support + batch_size = self._replay.batch_size + + # size of rewards: batch_size x 1 + rewards = self._replay.rewards[:, None] + + # size of tiled_support: batch_size x num_atoms + #tiled_support = tf.tile(_support, [batch_size]) + #tiled_support = tf.reshape(tiled_support, [batch_size, self._num_atoms]) + tiled_support = _support + + # size of target_support: batch_size x num_atoms + + is_terminal_multiplier = 1. - tf.cast(self._replay.terminals, tf.float32) + # Incorporate terminal state to discount factor. + # size of gamma_with_terminal: batch_size x 1 + gamma_with_terminal = self.cumulative_gamma * is_terminal_multiplier + gamma_with_terminal = gamma_with_terminal[:, None] + + target_support = rewards + gamma_with_terminal * tiled_support + + # size of next_qt_argmax: 1 x batch_size + next_qt_argmax = tf.argmax( + self._replay_next_target_net_outputs.q_values, axis=1)[:, None] + batch_indices = tf.range(tf.to_int64(batch_size))[:, None] + # size of next_qt_argmax: batch_size x 2 + batch_indexed_next_qt_argmax = tf.concat( + [batch_indices, next_qt_argmax], axis=1) + + # size of next_probabilities: batch_size x num_atoms + next_probabilities = tf.gather_nd( + self._replay_next_target_net_outputs.probabilities, + batch_indexed_next_qt_argmax) + + return project_distribution_1(target_support, next_probabilities, + _support) + else: + _support = self._support + batch_size = self._replay.batch_size + + # size of rewards: batch_size x 1 + rewards = self._replay.rewards[:, None] + + # size of tiled_support: batch_size x num_atoms + tiled_support = tf.tile(_support, [batch_size]) + tiled_support = tf.reshape(tiled_support, [batch_size, self._num_atoms]) + + # size of target_support: batch_size x num_atoms + + is_terminal_multiplier = 1. - tf.cast(self._replay.terminals, tf.float32) + # Incorporate terminal state to discount factor. + # size of gamma_with_terminal: batch_size x 1 + gamma_with_terminal = self.cumulative_gamma * is_terminal_multiplier + gamma_with_terminal = gamma_with_terminal[:, None] + + target_support = rewards + gamma_with_terminal * tiled_support + + # size of next_qt_argmax: 1 x batch_size + next_qt_argmax = tf.argmax( + self._replay_next_target_net_outputs.q_values, axis=1)[:, None] + batch_indices = tf.range(tf.to_int64(batch_size))[:, None] + # size of next_qt_argmax: batch_size x 2 + batch_indexed_next_qt_argmax = tf.concat( + [batch_indices, next_qt_argmax], axis=1) + + # size of next_probabilities: batch_size x num_atoms + next_probabilities = tf.gather_nd( + self._replay_next_target_net_outputs.probabilities, + batch_indexed_next_qt_argmax) + + return project_distribution(target_support, next_probabilities, + _support) + + + def _build_train_op(self): + """Builds a training op. + + Returns: + train_op: An op performing one step of training from replay data. + """ + target_distribution = tf.stop_gradient(self._build_target_distribution(self._replay_net_outputs.q_support)) + + # size of indices: batch_size x 1. + indices = tf.range(tf.shape(self._replay_net_outputs.probabilities)[0])[:, None] + # size of reshaped_actions: batch_size x 2. + print ("replay_action.shape, ", self._replay.actions.shape) + reshaped_actions = tf.concat([indices, self._replay.actions[:, None]], 1) + # For each element of the batch, fetch the logits for its selected action. + chosen_action_probabilities = tf.gather_nd(self._replay_net_outputs.probabilities, + reshaped_actions) + print ("----------------------------------------------------------") + print (self._replay_net_outputs.probabilities.shape, reshaped_actions.shape, chosen_action_probabilities.shape) + all_action_probabilities = self._replay_net_outputs.probabilities + cross_entropy = -1 * target_distribution * tf.log(chosen_action_probabilities + 1e-8) + loss = tf.reduce_sum(cross_entropy, axis=-1) + original_loss = loss + #loss = tf.reduce_mean(loss, axis=-1) + print (">>>>>>>>>>>>>>loss-prob:", loss.shape) + print (self._replay_net_outputs.a) + + if self._replay_net_outputs.a is not None: + chosen_pa = tf.gather_nd(self._replay_net_outputs.a, reshaped_actions) + pa = self._replay_net_outputs.a + + ''' + # size of indices: batch_size x 1. + indices = tf.range(tf.shape(self._replay_net_outputs.logits)[0])[:, None] + # size of reshaped_actions: batch_size x 2. + reshaped_actions = tf.concat([indices, self._replay.actions[:, None]], 1) + # For each element of the batch, fetch the logits for its selected action. + chosen_action_logits = tf.gather_nd(self._replay_net_outputs.logits, + reshaped_actions) + + loss1 = tf.nn.softmax_cross_entropy_with_logits( + labels=target_distribution, + logits=chosen_action_logits) + print (">>>>>>>>>>>>>>loss-logits:", loss1.shape) + ''' + + if self._replay_scheme == 'prioritized': + # The original prioritized experience replay uses a linear exponent + # schedule 0.4 -> 1.0. Comparing the schedule to a fixed exponent of 0.5 + # on 5 games (Asterix, Pong, Q*Bert, Seaquest, Space Invaders) suggested + # a fixed exponent actually performs better, except on Pong. + probs = self._replay.transition['sampling_probabilities'] + loss_weights = 1.0 / tf.sqrt(probs + 1e-10) + loss_weights /= tf.reduce_max(loss_weights) + + # Rainbow and prioritized replay are parametrized by an exponent alpha, + # but in both cases it is set to 0.5 - for simplicity's sake we leave it + # as is here, using the more direct tf.sqrt(). Taking the square root + # "makes sense", as we are dealing with a squared loss. + # Add a small nonzero value to the loss to avoid 0 priority items. While + # technically this may be okay, setting all items to 0 priority will cause + # troubles, and also result in 1.0 / 0.0 = NaN correction terms. + update_priorities_op = self._replay.tf_set_priority( + self._replay.indices, tf.sqrt(loss + 1e-10)) + + # Weight the loss by the inverse priorities. + loss = loss_weights * loss + else: + update_priorities_op = tf.no_op() + + with tf.control_dependencies([update_priorities_op]): + if self.summary_writer is not None: + with tf.variable_scope('Losses'): + tf.summary.scalar('CrossEntropyLoss', tf.reduce_mean(loss)) + # Schaul et al. reports a slightly different rule, where 1/N is also + # exponentiated by beta. Not doing so seems more reasonable, and did not + # impact performance in our experiments. + var = tf.trainable_variables() + print ("all trainable var ----------------------", var) + return self.optimizer.minimize(tf.reduce_mean(loss)), loss, chosen_action_probabilities,\ + target_distribution, self._replay.states, all_action_probabilities + + def _store_transition(self, + last_observation, + action, + reward, + is_terminal, + priority=None): + """Stores a transition when in training mode. + + Executes a tf session and executes replay buffer ops in order to store the + following tuple in the replay buffer (last_observation, action, reward, + is_terminal, priority). + + Args: + last_observation: Last observation, type determined via observation_type + parameter in the replay_memory constructor. + action: An integer, the action taken. + reward: A float, the reward. + is_terminal: Boolean indicating if the current state is a terminal state. + priority: Float. Priority of sampling the transition. If None, the default + priority will be used. If replay scheme is uniform, the default priority + is 1. If the replay scheme is prioritized, the default priority is the + maximum ever seen [Schaul et al., 2015]. + """ + if priority is None: + if self._replay_scheme == 'uniform': + priority = 1. + else: + priority = self._replay.memory.sum_tree.max_recorded_priority + + if not self.eval_mode: + self._replay.add(last_observation, action, reward, is_terminal, priority) + + +def project_distribution(supports, weights, target_support, + validate_args=False): + """Projects a batch of (support, weights) onto target_support. + + Based on equation (7) in (Bellemare et al., 2017): + https://arxiv.org/abs/1707.06887 + In the rest of the comments we will refer to this equation simply as Eq7. + + This code is not easy to digest, so we will use a running example to clarify + what is going on, with the following sample inputs: + + * supports = [[0, 2, 4, 6, 8], + [1, 3, 4, 5, 6]] + * weights = [[0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.2, 0.5, 0.1, 0.1]] + * target_support = [4, 5, 6, 7, 8] + + In the code below, comments preceded with 'Ex:' will be referencing the above + values. + + Args: + supports: Tensor of shape (batch_size, num_dims) defining supports for the + distribution. + weights: Tensor of shape (batch_size, num_dims) defining weights on the + original support points. Although for the CategoricalDQN agent these + weights are probabilities, it is not required that they are. + target_support: Tensor of shape (num_dims) defining support of the projected + distribution. The values must be monotonically increasing. Vmin and Vmax + will be inferred from the first and last elements of this tensor, + respectively. The values in this tensor must be equally spaced. + validate_args: Whether we will verify the contents of the + target_support parameter. + + Returns: + A Tensor of shape (batch_size, num_dims) with the projection of a batch of + (support, weights) onto target_support. + + Raises: + ValueError: If target_support has no dimensions, or if shapes of supports, + weights, and target_support are incompatible. + """ + target_support_deltas = target_support[1:] - target_support[:-1] + # delta_z = `\Delta z` in Eq7. + delta_z = target_support_deltas[0] + validate_deps = [] + supports.shape.assert_is_compatible_with(weights.shape) + supports[0].shape.assert_is_compatible_with(target_support.shape) + target_support.shape.assert_has_rank(1) + if validate_args: + # Assert that supports and weights have the same shapes. + validate_deps.append( + tf.Assert( + tf.reduce_all(tf.equal(tf.shape(supports), tf.shape(weights))), + [supports, weights])) + # Assert that elements of supports and target_support have the same shape. + validate_deps.append( + tf.Assert( + tf.reduce_all( + tf.equal(tf.shape(supports)[1], tf.shape(target_support))), + [supports, target_support])) + # Assert that target_support has a single dimension. + validate_deps.append( + tf.Assert( + tf.equal(tf.size(tf.shape(target_support)), 1), [target_support])) + # Assert that the target_support is monotonically increasing. + validate_deps.append( + tf.Assert(tf.reduce_all(target_support_deltas > 0), [target_support])) + # Assert that the values in target_support are equally spaced. + validate_deps.append( + tf.Assert( + tf.reduce_all(tf.equal(target_support_deltas, delta_z)), + [target_support])) + + with tf.control_dependencies(validate_deps): + # Ex: `v_min, v_max = 4, 8`. + v_min, v_max = target_support[0], target_support[-1] + # Ex: `batch_size = 2`. + batch_size = tf.shape(supports)[0] + # `N` in Eq7. + # Ex: `num_dims = 5`. + num_dims = tf.shape(target_support)[0] + # clipped_support = `[\hat{T}_{z_j}]^{V_max}_{V_min}` in Eq7. + # Ex: `clipped_support = [[[ 4. 4. 4. 6. 8.]] + # [[ 4. 4. 4. 5. 6.]]]`. + clipped_support = tf.clip_by_value(supports, v_min, v_max)[:, None, :] + # Ex: `tiled_support = [[[[ 4. 4. 4. 6. 8.] + # [ 4. 4. 4. 6. 8.] + # [ 4. 4. 4. 6. 8.] + # [ 4. 4. 4. 6. 8.] + # [ 4. 4. 4. 6. 8.]] + # [[ 4. 4. 4. 5. 6.] + # [ 4. 4. 4. 5. 6.] + # [ 4. 4. 4. 5. 6.] + # [ 4. 4. 4. 5. 6.] + # [ 4. 4. 4. 5. 6.]]]]`. + tiled_support = tf.tile([clipped_support], [1, 1, num_dims, 1]) + # Ex: `reshaped_target_support = [[[ 4.] + # [ 5.] + # [ 6.] + # [ 7.] + # [ 8.]] + # [[ 4.] + # [ 5.] + # [ 6.] + # [ 7.] + # [ 8.]]]`. + reshaped_target_support = tf.tile(target_support[:, None], [batch_size, 1]) + reshaped_target_support = tf.reshape(reshaped_target_support, + [batch_size, num_dims, 1]) + # numerator = `|clipped_support - z_i|` in Eq7. + # Ex: `numerator = [[[[ 0. 0. 0. 2. 4.] + # [ 1. 1. 1. 1. 3.] + # [ 2. 2. 2. 0. 2.] + # [ 3. 3. 3. 1. 1.] + # [ 4. 4. 4. 2. 0.]] + # [[ 0. 0. 0. 1. 2.] + # [ 1. 1. 1. 0. 1.] + # [ 2. 2. 2. 1. 0.] + # [ 3. 3. 3. 2. 1.] + # [ 4. 4. 4. 3. 2.]]]]`. + numerator = tf.abs(tiled_support - reshaped_target_support) + quotient = 1 - (numerator / delta_z) + # clipped_quotient = `[1 - numerator / (\Delta z)]_0^1` in Eq7. + # Ex: `clipped_quotient = [[[[ 1. 1. 1. 0. 0.] + # [ 0. 0. 0. 0. 0.] + # [ 0. 0. 0. 1. 0.] + # [ 0. 0. 0. 0. 0.] + # [ 0. 0. 0. 0. 1.]] + # [[ 1. 1. 1. 0. 0.] + # [ 0. 0. 0. 1. 0.] + # [ 0. 0. 0. 0. 1.] + # [ 0. 0. 0. 0. 0.] + # [ 0. 0. 0. 0. 0.]]]]`. + clipped_quotient = tf.clip_by_value(quotient, 0, 1) + # Ex: `weights = [[ 0.1 0.6 0.1 0.1 0.1] + # [ 0.1 0.2 0.5 0.1 0.1]]`. + weights = weights[:, None, :] + # inner_prod = `\sum_{j=0}^{N-1} clipped_quotient * p_j(x', \pi(x'))` + # in Eq7. + # Ex: `inner_prod = [[[[ 0.1 0.6 0.1 0. 0. ] + # [ 0. 0. 0. 0. 0. ] + # [ 0. 0. 0. 0.1 0. ] + # [ 0. 0. 0. 0. 0. ] + # [ 0. 0. 0. 0. 0.1]] + # [[ 0.1 0.2 0.5 0. 0. ] + # [ 0. 0. 0. 0.1 0. ] + # [ 0. 0. 0. 0. 0.1] + # [ 0. 0. 0. 0. 0. ] + # [ 0. 0. 0. 0. 0. ]]]]`. + inner_prod = clipped_quotient * weights + # Ex: `projection = [[ 0.8 0.0 0.1 0.0 0.1] + # [ 0.8 0.1 0.1 0.0 0.0]]`. + projection = tf.reduce_sum(inner_prod, 3) + projection = tf.reshape(projection, [batch_size, num_dims]) + return projection + + +def project_distribution_1(supports, weights, target_support, + validate_args=False): + """Projects a batch of (support, weights) onto target_support. + + Based on equation (7) in (Bellemare et al., 2017): + https://arxiv.org/abs/1707.06887 + In the rest of the comments we will refer to this equation simply as Eq7. + + This code is not easy to digest, so we will use a running example to clarify + what is going on, with the following sample inputs: + + * supports = [[0, 2, 4, 6, 8], + [1, 3, 4, 5, 6]] + * weights = [[0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.2, 0.5, 0.1, 0.1]] + * target_support = [4, 5, 6, 7, 8] + + In the code below, comments preceded with 'Ex:' will be referencing the above + values. + + Args: + supports: Tensor of shape (batch_size, num_dims) defining supports for the + distribution. + weights: Tensor of shape (batch_size, num_dims) defining weights on the + original support points. Although for the CategoricalDQN agent these + weights are probabilities, it is not required that they are. + target_support: Tensor of shape (num_dims) defining support of the projected + distribution. The values must be monotonically increasing. Vmin and Vmax + will be inferred from the first and last elements of this tensor, + respectively. The values in this tensor must be equally spaced. + validate_args: Whether we will verify the contents of the + target_support parameter. + + Returns: + A Tensor of shape (batch_size, num_dims) with the projection of a batch of + (support, weights) onto target_support. + + Raises: + ValueError: If target_support has no dimensions, or if shapes of supports, + weights, and target_support are incompatible. + """ + target_support_deltas = target_support[0,1:] - target_support[0,:-1] + # delta_z = `\Delta z` in Eq7. + delta_z = target_support_deltas[0] + validate_deps = [] + supports.shape.assert_is_compatible_with(weights.shape) + supports[0].shape.assert_is_compatible_with(target_support[0].shape) + target_support.shape.assert_has_rank(2) + if validate_args: + # Assert that supports and weights have the same shapes. + validate_deps.append( + tf.Assert( + tf.reduce_all(tf.equal(tf.shape(supports), tf.shape(weights))), + [supports, weights])) + # Assert that elements of supports and target_support have the same shape. + validate_deps.append( + tf.Assert( + tf.reduce_all( + tf.equal(tf.shape(supports)[1], tf.shape(target_support[0]))), + [supports, target_support[0]])) + # Assert that target_support has a single dimension. + validate_deps.append( + tf.Assert( + tf.equal(tf.size(tf.shape(target_support[0])), 1), [target_support[0]])) + # Assert that the target_support is monotonically increasing. + validate_deps.append( + tf.Assert(tf.reduce_all(target_support_deltas > 0), [target_support[0]])) + # Assert that the values in target_support are equally spaced. + validate_deps.append( + tf.Assert( + tf.reduce_all(tf.equal(target_support_deltas, delta_z)), + [target_support[0]])) + + with tf.control_dependencies(validate_deps): + # Ex: `v_min, v_max = 4, 8`. + v_min, v_max = target_support[:,0], target_support[:,-1] + # Ex: `batch_size = 2`. + batch_size = tf.shape(supports)[0] + # `N` in Eq7. + # Ex: `num_dims = 5`. + num_dims = tf.shape(target_support[0])[0] + # clipped_support = `[\hat{T}_{z_j}]^{V_max}_{V_min}` in Eq7. + # Ex: `clipped_support = [[[ 4. 4. 4. 6. 8.]] + # [[ 4. 4. 4. 5. 6.]]]`. + #TODO + #clipped_support = tf.map_fn(fn=lambda inp: tf.clip_by_value(inp[0], inp[1], inp[2]), elems=[supports, v_min, v_max], dtype=tf.float32) + v_min1 = tf.tile(tf.reshape(v_min, [-1, 1]), [1, num_dims]) + v_max1 = tf.tile(tf.reshape(v_max, [-1, 1]), [1, num_dims]) + clipped_support = tf.minimum(tf.maximum(supports, v_min1), v_max1) + #clipped_support = tf.clip_by_value(supports, v_min[0], v_max[0]) + print ('----', clipped_support.shape) + clipped_support = clipped_support[:, None, :] + print ('----', clipped_support.shape) + # Ex: `tiled_support = [[[[ 4. 4. 4. 6. 8.] + # [ 4. 4. 4. 6. 8.] + # [ 4. 4. 4. 6. 8.] + # [ 4. 4. 4. 6. 8.] + # [ 4. 4. 4. 6. 8.]] + # [[ 4. 4. 4. 5. 6.] + # [ 4. 4. 4. 5. 6.] + # [ 4. 4. 4. 5. 6.] + # [ 4. 4. 4. 5. 6.] + # [ 4. 4. 4. 5. 6.]]]]`. + tiled_support = tf.tile([clipped_support], [1, 1, num_dims, 1]) + # Ex: `reshaped_target_support = [[[ 4.] + # [ 5.] + # [ 6.] + # [ 7.] + # [ 8.]] + # [[ 4.] + # [ 5.] + # [ 6.] + # [ 7.] + # [ 8.]]]`. + reshaped_target_support = target_support #tf.tile(target_support[:, None], [batch_size, 1]) + reshaped_target_support = tf.reshape(reshaped_target_support, + [batch_size, num_dims, 1]) + # numerator = `|clipped_support - z_i|` in Eq7. + # Ex: `numerator = [[[[ 0. 0. 0. 2. 4.] + # [ 1. 1. 1. 1. 3.] + # [ 2. 2. 2. 0. 2.] + # [ 3. 3. 3. 1. 1.] + # [ 4. 4. 4. 2. 0.]] + # [[ 0. 0. 0. 1. 2.] + # [ 1. 1. 1. 0. 1.] + # [ 2. 2. 2. 1. 0.] + # [ 3. 3. 3. 2. 1.] + # [ 4. 4. 4. 3. 2.]]]]`. + numerator = tf.abs(tiled_support - reshaped_target_support) + quotient = 1 - (numerator / delta_z) + # clipped_quotient = `[1 - numerator / (\Delta z)]_0^1` in Eq7. + # Ex: `clipped_quotient = [[[[ 1. 1. 1. 0. 0.] + # [ 0. 0. 0. 0. 0.] + # [ 0. 0. 0. 1. 0.] + # [ 0. 0. 0. 0. 0.] + # [ 0. 0. 0. 0. 1.]] + # [[ 1. 1. 1. 0. 0.] + # [ 0. 0. 0. 1. 0.] + # [ 0. 0. 0. 0. 1.] + # [ 0. 0. 0. 0. 0.] + # [ 0. 0. 0. 0. 0.]]]]`. + clipped_quotient = tf.clip_by_value(quotient, 0, 1) + # Ex: `weights = [[ 0.1 0.6 0.1 0.1 0.1] + # [ 0.1 0.2 0.5 0.1 0.1]]`. + weights = weights[:, None, :] + # inner_prod = `\sum_{j=0}^{N-1} clipped_quotient * p_j(x', \pi(x'))` + # in Eq7. + # Ex: `inner_prod = [[[[ 0.1 0.6 0.1 0. 0. ] + # [ 0. 0. 0. 0. 0. ] + # [ 0. 0. 0. 0.1 0. ] + # [ 0. 0. 0. 0. 0. ] + # [ 0. 0. 0. 0. 0.1]] + # [[ 0.1 0.2 0.5 0. 0. ] + # [ 0. 0. 0. 0.1 0. ] + # [ 0. 0. 0. 0. 0.1] + # [ 0. 0. 0. 0. 0. ] + # [ 0. 0. 0. 0. 0. ]]]]`. + inner_prod = clipped_quotient * weights + # Ex: `projection = [[ 0.8 0.0 0.1 0.0 0.1] + # [ 0.8 0.1 0.1 0.0 0.0]]`. + projection = tf.reduce_sum(inner_prod, 3) + projection = tf.reshape(projection, [batch_size, num_dims]) + return projection diff --git a/dopamine/colab/README.md b/dopamine/colab/README.md index 7f9bc0d..2c28c7e 100644 --- a/dopamine/colab/README.md +++ b/dopamine/colab/README.md @@ -1,31 +1,31 @@ -# Colabs - -This directory contains -[`utils.py`](https://github.com/google/dopamine/blob/master/dopamine/colab/utils.py), -which provides a number of useful utilities for loading experiment statistics. - -We also provide a set of colabs to help illustrate how you can use Dopamine. - -## Agents - -In this -[colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/agents.ipynb) -we illustrate how to create a new agent by either subclassing -[`DQN`](https://github.com/google/dopamine/blob/master/dopamine/agents/dqn/dqn_agent.py) -or by creating a new agent from scratch. - -## Loading statistics - -In this -[colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/load_statistics.ipynb) -we illustrate how to load and visualize the logs data produced by Dopamine. - -## Visualizing with Tensorboard -In this -[colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/tensorboard.ipynb) -we illustrate how to download and visualize different agents with Tensorboard. - -## Training on Cartpole -In this -[colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/cartpole.ipynb) -we illustrate how to train DQN and C51 on the Cartpole environment. +# Colabs + +This directory contains +[`utils.py`](https://github.com/google/dopamine/blob/master/dopamine/colab/utils.py), +which provides a number of useful utilities for loading experiment statistics. + +We also provide a set of colabs to help illustrate how you can use Dopamine. + +## Agents + +In this +[colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/agents.ipynb) +we illustrate how to create a new agent by either subclassing +[`DQN`](https://github.com/google/dopamine/blob/master/dopamine/agents/dqn/dqn_agent.py) +or by creating a new agent from scratch. + +## Loading statistics + +In this +[colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/load_statistics.ipynb) +we illustrate how to load and visualize the logs data produced by Dopamine. + +## Visualizing with Tensorboard +In this +[colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/tensorboard.ipynb) +we illustrate how to download and visualize different agents with Tensorboard. + +## Training on Cartpole +In this +[colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/cartpole.ipynb) +we illustrate how to train DQN and C51 on the Cartpole environment. diff --git a/dopamine/colab/__init__.py b/dopamine/colab/__init__.py index 920cbb5..dc108ba 100644 --- a/dopamine/colab/__init__.py +++ b/dopamine/colab/__init__.py @@ -1,15 +1,15 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/dopamine/colab/agents.ipynb b/dopamine/colab/agents.ipynb index 98f271f..c087022 100644 --- a/dopamine/colab/agents.ipynb +++ b/dopamine/colab/agents.ipynb @@ -1,394 +1,394 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "agents.ipynb", - "version": "0.3.2", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - } - }, - "cells": [ - { - "metadata": { - "id": "VYNA79KmgvbY", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Copyright 2018 The Dopamine Authors.\n", - "\n", - "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", - "\n", - "https://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." - ] - }, - { - "metadata": { - "id": "emUEZEvldNyX", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# Dopamine: How to create and train a custom agent\n", - "\n", - "This colab demonstrates how to create a variant of a provided agent (Example 1) and how to create a new agent from\n", - "scratch (Example 2).\n", - "\n", - "Run all the cells below in order." - ] - }, - { - "metadata": { - "id": "Ckq6WG-seC7F", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Install necessary packages.\n", - "!pip install --upgrade --no-cache-dir dopamine-rl\n", - "!pip install cmake\n", - "!pip install atari_py\n", - "!pip install gin-config" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "WzwZoRKxdFov", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Necessary imports and globals.\n", - "\n", - "import numpy as np\n", - "import os\n", - "from dopamine.agents.dqn import dqn_agent\n", - "from dopamine.discrete_domains import run_experiment\n", - "from dopamine.colab import utils as colab_utils\n", - "from absl import flags\n", - "import gin.tf\n", - "\n", - "BASE_PATH = '/tmp/colab_dope_run' # @param\n", - "GAME = 'Asterix' # @param" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "EFY3tTITHugq", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Load baseline data\n", - "!gsutil -q -m cp -R gs://download-dopamine-rl/preprocessed-benchmarks/* /content/\n", - "experimental_data = colab_utils.load_baselines('/content')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "bidurBV0djGi", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "## Example 1: Train a modified version of DQN\n", - "Asterix is one of the standard agents provided with Dopamine.\n", - "The purpose of this example is to demonstrate how one can modify an existing agent. The modification\n", - "we are doing here (choosing actions randomly) is for illustrative purposes: it will clearly perform very\n", - "poorly." - ] - }, - { - "metadata": { - "id": "PUBRSmX6dfa3", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Create an agent based on DQN, but choosing actions randomly.\n", - "\n", - "LOG_PATH = os.path.join(BASE_PATH, 'random_dqn', GAME)\n", - "\n", - "class MyRandomDQNAgent(dqn_agent.DQNAgent):\n", - " def __init__(self, sess, num_actions):\n", - " \"\"\"This maintains all the DQN default argument values.\"\"\"\n", - " super(MyRandomDQNAgent, self).__init__(sess, num_actions)\n", - " \n", - " def step(self, reward, observation):\n", - " \"\"\"Calls the step function of the parent class, but returns a random action.\n", - " \"\"\"\n", - " _ = super(MyRandomDQNAgent, self).step(reward, observation)\n", - " return np.random.randint(self.num_actions)\n", - "\n", - "def create_random_dqn_agent(sess, environment, summary_writer=None):\n", - " \"\"\"The Runner class will expect a function of this type to create an agent.\"\"\"\n", - " return MyRandomDQNAgent(sess, num_actions=environment.action_space.n)\n", - "\n", - "random_dqn_config = \"\"\"\n", - "import dopamine.discrete_domains.atari_lib\n", - "import dopamine.discrete_domains.run_experiment\n", - "atari_lib.create_atari_environment.game_name = '{}'\n", - "atari_lib.create_atari_environment.sticky_actions = True\n", - "run_experiment.Runner.num_iterations = 200\n", - "run_experiment.Runner.training_steps = 10\n", - "run_experiment.Runner.max_steps_per_episode = 100\n", - "\"\"\".format(GAME)\n", - "gin.parse_config(random_dqn_config, skip_unknown=False)\n", - "\n", - "# Create the runner class with this agent. We use very small numbers of steps\n", - "# to terminate quickly, as this is mostly meant for demonstrating how one can\n", - "# use the framework.\n", - "random_dqn_runner = run_experiment.TrainRunner(LOG_PATH, create_random_dqn_agent)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "WuWFGwGHfkFp", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Train MyRandomDQNAgent.\n", - "print('Will train agent, please be patient, may be a while...')\n", - "random_dqn_runner.run_experiment()\n", - "print('Done training!')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "IknanILXX4Zz", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Load the training logs.\n", - "random_dqn_data = colab_utils.read_experiment(LOG_PATH, verbose=True)\n", - "random_dqn_data['agent'] = 'MyRandomDQN'\n", - "random_dqn_data['run_number'] = 1\n", - "experimental_data[GAME] = experimental_data[GAME].merge(random_dqn_data,\n", - " how='outer')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "mSOVFUKN-kea", - "colab_type": "code", - "outputId": "c7053a43-9f59-4817-ee0a-b3c074b509b1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 512 - } - }, - "cell_type": "code", - "source": [ - "# @title Plot training results.\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,8))\n", - "sns.tsplot(data=experimental_data[GAME], time='iteration', unit='run_number',\n", - " condition='agent', value='train_episode_returns', ax=ax)\n", - "plt.title(GAME)\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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+jl5SStk3vomi9rLPVVXwjqh2P2s4jBUKYUcj4LgZZK2gIOM+3mTh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m5wv09QbYu9OdM3VyWYwL74Omoxx7svLBBJBjMZxCgcnpKUQRmmOLVQ1RVZGD9XNbl7L/WBdz\nMxkejMxz9eIYu/a3lxak4/cTzE1n6OqJluZRV8POvW2M3J1l+I77T/fJPPXCzlJL8b7DHYTCGh++\ne493fzbA8Sd72L6rZdXHWUoiniMRz9G5LVohhto7w9y+NsnMVKqhyB3HcRgamEaSRbb2Ls69Hzy+\nldHhOJ+cH0XTZD4+O0IqmScY1njsdG/NWKCt22NMPkhy7/YMQwMzCAKuuVQDC3VVk5FliUhMJz5b\nf2ZUUSSefrGfsZF52jvDZZX4rb0xdu1v59bVCT589x6Pne7h47MjiKJQ0+xqo1qEN5Ja1d0i/ioV\nP1EUCIY1kvP1nZxXUzGUZQlFlVb8HAuGNTq2RhFkwd2Es1yzt0Y++2qeo18p/Z6RJh/xmcyao9FW\nQtPlCkGp++SGq+XhqL5qgdwIoiigqFLd94KHR13Be+fOHb7//e+XfnYch2984xubelIeHh4ejzJF\nwfv26BnOPPgQRZT5pe0vsb95D59MX+PVe69xYeoyX9h6uux+dxPDjGcm2R3r54mOx2o+vl/xlcQu\nUFZddPNl7TXFHOSyiwtax3ENo6rN39YyyLKs2lVX23YrtNFmf8X9ViNCiwus1Toi5/NmtVHYVWHb\nDrmsgSyLK1YpHMfBsR1EScSKJypmcMfGc8zPFxAEuHMvTTJlcuxgFOf8+5DPoZx6HkGv3LSQfAFE\nRSFruDFELU3qYpurKCBFG48ZKiKKAief3c7P/u4GNy6Pc+PyOJouE4roJSfWpbO7q0FYEFCvv3KD\nQFDlqRd3VpiPdW9vwh/UeO+1Ac6fGXLNm7au/vcosujOXOlw3LYgeCfGkg0J3unJFOlkgZ6+JpQl\n4tkfVNl9YAvXLz1wq7oC7Nrfzv6jnSuacHVti3HxzHApo3f3wS0NbyQU280V1X1tGhFssiKxbUd1\nQ7IDx7uYm0kzNhzn9XiWXNZg/9FOQpHqG2zBsIYsizU3dyRJJNrsK3O83UxEcfUVxiK6TyGbWdns\nrpjluhr8AZX5QvXNCFEUShXSpdmyuk8hncyv2FFTC1WTyz6fZVkiHPVtiolWNUGpqHLdVmpBcMcn\nNtMMKhRZW0eTx88Xdd+B7e3t5PN5NG1h/qlQoLv70TAz8PDw8HjY2I5NGs3zIwAAIABJREFU3i4Q\nz89z5sGHRLUwv9z/FVp9bpvg3qZdvHH/XS5NXeV0xwmUJVXa9x+cA+DJjhXGRgSIqOWLdWPZwi2X\nMarGaKx43lXmULMZA59fLVvEm4ZFYj6HYzv4gyq6bzESpF5UhWnaJOJZwlFf2QIln1vdYm8tC2rT\nsLHM1d0vEc9y9eIYqUSeXNYoVbhUTeIXfvlAzUXcR+/eY2IswbNf7EUtlFeibdvhxkASQYCnTrZw\n43aSyek875yZ5NCtWwRCEeQDxysfVBSQQm7lcGKsmJO6mJggBYNls8GrQdMVnvnSLgZvTZGM50jM\n55iedHOBt+1oWtd8bbTJzy/8owNomlxTDDa3Bjj9xT7e+vEtPnhrkOe+vGfNxxwdiiMI0FFFNLe2\nBxFEYSFn1o1jcqu4M9y/N8e+I500tS5WyIduu2ZVPf2VVefdB9oZuTtbqooun9mthqbLtHWEmRhL\nEAxp7DvcWJu4JItl4kv3KViWva5oGHejYwev/d11Uok84ajO7gPVc5NVze308Ac1DKPyc0IQBCIx\nX8nxVpSEDY+tkRX3OZBlccEkbX0O0qGwxtxMbSfntRiIqZpcEZNTxBeo7okgCALBsI6sSA1tYhSR\nFZFwtHJzQtUa3xDRdBnDsOrO/rqZttU/63Rf7YgiQRCINPnKNos2g9VuTHj8fFJX8DqOwwsvvMCx\nY8dwHIdLly6xc+dOfvu3fxuA3/3d3930k/Tw8PB4VMiZOXDgk+nrAJzuOFkSuwCyKHOk9SBnHnzI\ntdmbHG49AMBIcpTR1AP6I9tp99ee6QvIgZJILrK8JS67BsFbq10xlcyXxEcmXShzRU4l8mTSBYIh\nDVmWGhKihbxVaksGVwBudJtxcj7H4K0p+va0EVxSUVzJfXopjuNw+9okV86PYtsOsiyi+xVCER3H\ncZiZTDN4c4o9hyoFS3I+V5r7/PC9YZ483lTWCjh8P0Mma9Hb7ScaVjh5LMa1W0kGh9J82PWLPNaZ\nwydXfjVLwRCC5C4cJ0bdedfOvnYkIYdtmEiB9WWmBsMahx7bWvrZMm03QmedzsFQvbV0Oc1tQR5/\nqpezb93lvdcGeP7v7Vm1cdLgzSniMxm2bI1U3YyQFYnm1gDTEykKeRPbdjj//hAPRtznc2Iswb4j\nnew5uAXLshm5N4c/qFbNo5UViZf+4X4EYXVZlH17W5mdSnP8dE/DkUxqFTOxQFDDMu11tRnrPoVT\nz/dx9cIYB453IdYQDrpv8bkMRXTmZtJlIikS08t+l0BQQ5LEMtElySK6T0GWxVVVIHWf27K7nviq\nasiKhC+gVu1IKbaPrwVfQKnIBRcEoW7Lre5z82MT8WxdUz1JFonEfDXfd7pPwbYd8lkD23bKHk/V\nZHSfvGA65d4/nzPJZgo1W6tX+jusJXhFSSAa82/46+bhsVbqCt4XX3yRF198sfTzc889t6kn5OHh\n4fEokzXzOI7DlZnrqKLCrlh/xW2Oth7k7Ph5Ppq8xKGW/QiCwPsPPgQaqO5qlTnoyxcqtu2aT62m\n5a+WWC3kTfI5g5mpVNUIINtySMRzqzJjyWUNJEnAH9RW3Zq8EpZpc+OTcW5+Mr7QQp3j6Zd21rz9\nzGQKWZEIhrTSwiydyvPRu/eYGk+h6TLHnuwpa481Chb/568vc/vaJDv3tVcs6G5dmQAgEFCYnStw\nezDF7v4QjmlgIXFrMIUkCeza4YooQRDY25RBO/cuN9pOcXHOx7N5C01bXHALsoQUcCuPju0wMZbA\nt2DCBDqSZW94S58kizXbWzeL7u1NpJJ5rl4Y473XB3j2F3Y35EwMbv7txbMjqJrM0ZO1u9DaOsNM\nT6S4cn6U+0NxCnmT1i1Bduxq5fJH97l6cYyJsQTtnWEs06anr7nmc7sWk5zO7ih//1uHV/V61eok\nCIa1srn7tdDUEljxb0QQyo8vigKRqK9UHQ1F9KrVPzcuSKCQN12hu6wlvF4F2OdX8AXUTa3eBYJq\nSRSC+7coyeK6NnmKLcpLXxOfX2no9VZUiViLn1zGIJsxKoSvJImlrpp6+ANq2UbTSjntroO1jGla\n5LMm5oLJX7FSvXTDYzlu9XdxdlmUBHw+Bb1Bwy8Pj4dFXcH71a9+lVu3bjE8PMwLL7xAIpEgHK4/\n++Lh4eHx80jOyjGUvE+ikORQy76yWdsiITXI7lg/12dvMZy8jyzKDCVH6A130xmsHZkSUoLIYvnH\ntmlaVSuX2Uzjgtc0rRVdhhPxHGJ05cXLahfd6VQBURI3zAhnfHSeix+MkE7m8fkVVE1mYizB5IMk\nbR2VmwRDAzN8+O690s/+oEoorDMzlcI0bDq7Ixw71VOxuFRUiR27W7l1ZYKhwRl27FqsxmczBkN3\nZgiGVE4fj/H2B9PcGkwRzU8Sevd/M7TzBfLmFnbtCJYEreM4FM68QWfyHs7Bk9yYFPn46jwnjsZK\ni1MpHC79/+xMhkLeYvvOxetZZ3vnZ4k9B7eQms8zdGeGs28N0r+3jXDUh+6rzDAukkkXOPPGHXAc\nnvjCjoo54aW0d4S4dhEGb00jSgKHH99K/742BEGgrTPMhTNDjA7FmZ5wjcZ6V4h2Wiur3ZyoFhcF\nlNqMG8miXiuaXinWZEUiFNGxLHtF8aVqclWx7g+oFHKusFqOILht8LXclzcSQRAIx3w4tlPRNr6e\nx9T95ZXjRqKNihRfU3/QjVXKZQtYloM/0JjQXem86iHLEnJo8Xl3HAfbcuoKV3esBXSfWjfv1sPj\n06LuO/N//s//ySuvvEKhUOCFF17gD//wDwmHw/z6r//6wzg/Dw8Pj0cGwzKwbItPpq8BcLB5X83b\nHm87zPXZW5yfvIS1EGF0quNEzdsLgkBYrV/dXXq5aVoNtebls5+O++pqZtZW4vJH97l1ZQJBgJ37\n29h3pJPkfI7XX7nBlQujPPfl3ctmhk0ufXgfSRbZtqOJ5HyOVCLHxFgCWRF57HQPPf21K3s797Ux\ncG2SW1cm2N7fUnLZvX1tAtt26OsNoqoixw5Fef/cNJeGHI6Jfu7mm1Bkix29i3Oi9shd7Pv3ELu3\n03+4m5kLc0xO5xkcStPXG0RUNaQlBlbFdub2rs/nxrMgCBw/tc2N7hmZL7UbK6orsto6Qmzf2VIS\ntcV4nXzO5PCJ7qqbG0uJtQSIxHxIsvs6h5c4mmu6zBNf2MG92zN8fG6Eto7QiuL5YaCo0opixedX\nyGWNVUUtrYZa1b31iq9gRCdeZYY2EvM9FLFbZDPmS31+pSR4G402qkax8vpp4Va86wtl3aes6/3g\n4fEwqPuX9Morr/BXf/VXvPzyywD89m//Nr/yK7/iCV4PDw+PZeSsPHkzz625AWJalK5gFVMaAXCg\nM7CFLf42bscHAegOdtIdco10NFnDcWwK1mKbcUgNIomVi7OVIi3SyXyFQVTV894EV9Wp8SSmaVc1\nD9pI4jMZbl2ZIBjWeOILO0qut00tATq3RRkbjvPg/jyd3YttyZ+cv08hb3LwsS52H1isqBsFC1EU\n6s6d+fwq2/qauHd7hrGROF09MYyCxeDNKTRdoqvNbSWMmnF2xC9zJ3qYD3v/IZYjsmPqHOJYHrb1\n4dg2hTNvAKA+6eYsHz0Q4a0z01y/naS5SaV1R7lh0vhoAkGgrrB7lBElkadf3MnocJxEPEty3o0a\nmptOMzuV5sblcVf47mph/P48c9MZevqb6d9bP89WFAVe+Pt7a/5NCILA9l0tdG+PbUibeC0To0ap\nNr+7FEEQCIW1hqKKVosoCTXNitaLokj4AyqZJZXQcNS3acd7mEiSWIpxWk20kYeHx+ZR95MlEAiU\n7U6Jouj15Xt4eHyuKVjuIkyVGl+spI0M8/kE1+duYzoWB1sqF9WapBJUg8xkZ7FMh33+/YxnJgF4\not2d3RUEgRa9CUmUXMdnK0/WyBOUq+erFgXv/FwWURIILYmpKOQtkvO5sipW5f3NuiYpq6WQN3nv\nZwNYps0XfnF3Qw624FbrBm5Momky4aiPcFSvW+355PwoAEdObquIeNl/tJOx4ThXL4zRsTWCIAhM\nTbg5qJGYj537yl1pa7WOVmPX/i3cuz3DzU8m6NwW5c7NKUzDZuf2MJIkYCfi5F/5S3oyaeJb9jGT\nU/ApDluTt8n/5A76L/8q9uQDnJlJpN0HEFvcc9E0iaMHo3xwfpYLnyR4YUc32A4zUykejMwzO52m\nuTW4qTEfnwWK1felmIbF6FCcu7enmXyQZPJBEoBYi59jT2xrWKA21N65QZU/f1AlnzPWbMymNPA6\nK6q8KTm5+ibkpi7FH1TJ500s0yYU0T9X7bBFkyrPQdjD47NB3U+Xbdu28Qd/8AckEgl+8pOf8Oqr\nr9LX1/cwzs3Dw8PjUyFtZEgbGdr8rVVncJdi2CZzuTlypjtH98n0NQQE9jfvKbudKqm0+lsQEBjP\nOmRSBfp8O/hQ+oigHKQVt9Lol32lSq4oiGiiTi5jU7As5GD5R7Zl2SUXzrd+dAtRFPjFrx0oW2Tl\ncyapRK5qnq5pWhWOohvBnRtTpZngs2/f5cW/v68hMXn98gNuXB4vu8wfUOncFuXw41tLrcNFJsYS\nTIwlaOsMsaVKi28k5mNbXxPDd2a5f3eOrp4oF94fBuDYk9vWZDpUJBzV6eyOMDYyz+SDJAPXJpBl\nkZ4uH04mTf7v/gInk0I9/QLH9nTxyfV5ersD6F2/SOG1H5B/9a/BtkGSUE48U/bYrc0a/X1hBu4k\neOPVG+Syi4JJkoSa8TGfd2RFoqe/mZ7+ZhLxHPcGpknEsxx/cmXHY1WTN9QcrVEkyXUmdpy1OZGL\notBwy20gpG244NVWMCvaCARBIBTRMQrW564lVlFrx3B5eHg8fOp+mv3bf/tv+dM//VPa29v5wQ9+\nwPHjx/n2t7/9MM7Nw8PD41MhY2axHZvJzFRN0es4DolCkkQhWTKNmkzOMJYeZ3t4W9m8rSIptPlb\nEAU3dkLMK+DkkQSJb3R8HVEQsA2HfMakvXWxGmrbDvNzGUzDxjQKqHp5XEaxujs7lS4t6IcHZ9m+\ns7wNNpsxEAShNI/oOA7pVKFqJMd6MQ2L29cmUFSJnr5mBq5PcvGDYU48s33F+2XSBW5dnUD3K+w9\n1EFyPksiniM+m2Xg+iTgcPhEd6k65zgOn3x0H4CDx7fWfNx9RzoZGZzl6sUxkokcyfkcO3a3NFx1\nXoldB7cwNjLPB28OYhQs+noDKIpI7sev4MzPIR87hXL4cRTg8SML1crm/dhz05jn3wdAPvoEYmhZ\n27cAB070MJsYZHYqjc+vsGNXjI7uKG0dIW8hjbvhsDRGqRbF2d/ZqXTDsVQbRdHtV9PlNW0srabj\nQJJEAkGV9Abl38rK+rNuG0FRpE3Paf208LohPTw+O9QVvIqi8Gu/9mv82q/9WsV1v/mbv8nv/d7v\nbcqJeXh4eGwGWTOHKipl87BLzZ1yZg7LdoWk7dhMZadp87WUsm8t2yJppEgV0tjO4mxePmNy8cEV\nAA62LJpVyaJMm88Vu+lU3p3rknVSRgocUJa4LptZEG0JJFfQzc9ly9yTk/N5Ys2LbbtFwTu+YGQE\nbixObxXDpUy6gCAKyLJIMpEry9FcysRYAoD2zrWZIg3emqaQt9h7uIO9hzuYnUozPDhLe1eYnr7a\njrfXLo5hWw77j3aWCXajYPHGqzcYuD6FP6ixa79b3RwZnCU+m2Xbjqay52Q5wZDGjt2t3LkxxbWP\nH6DpMgeOdTX8+5jz8wiahqRXVshb2oI0twWZmUwhirCjJ4A1MYo9PIjYuQ3l5DNVHhGUE8/gpFPY\nE6MoR5+ouF4KhZA1lWde2kkmXSAU0dc1T6pqMj6/Qjy+5ocA3IqjpstkMxs/871Z+AMqoijgC1TP\nC10NkZiPRDzbkCO5KAmlFl1RLI9uaZTVtq37Aiq5nLkhBlariTTz8PDw+Kyzru2nycnJjToPDw8P\nj4dC2kiTMtKlnx3HIT6TLRk3Zcxy8xfLtpjMTpMxskxnZxlNPyCRT5aJXSNvkUkWuJW+jSqq9IXc\naqYkSrT7W5FEiULeLC24JVFClyoFlE/2kYznsG2b+GwW0yhfIJuGVWbyYixcPzGaQBAFunqiJOdz\nPLg/TzXSyTzzc9maYjebMXjvZwO8+9oAc9PpqrdZCcuyuXV1AkkW6d/bhigKnHhmO7IscvGDYVI1\n4lPm57LcG5ghHNXpXSaKFVXiqRd3ovsULn94n/v35rAsmysXxhBFgf1HO+ue155DHUiSKxgPn+hu\nWEhY6TRWOo05O4s5N4djVQqWXftco6StHT50TcL46D33vB9/ekVjJO35v4f+K/8MQS+frxZVBSng\nVp9lRWrIdCza5Me3IOyWIssikZhv4Z9/XS3cgiAQafIRDK88a6npMtFm/4q5zLIirjhXvlHIslh6\nrX1+dV2bBrrPjbpq9LwDQa3seGuZuV5NhRcWnNw3KDt5pexVDw8Pj0eNdQnejQ669/Dw8NhMbMcm\na+ZIGYvtjUbBzbFNzufIZgpkjEq3U8u2mM7OkDEysEwrWqbNbDzF+fkLZKwM/f5+CikHx3GIahEk\nUcKybBLx8ggev7KsKimI+GQ323J2Kl0hdoukk3ks053dtUybXNZgbiZDS3uQfUdc8XfrysSanp+b\nn4xjWw6O7XD27bs1z6EWQwMz5DIGfbtbS6IoGNY4+sQ2TMPm3Nt3qxpkXS62Jj9WOacLbpXu9Av9\nSLLIuXfucuH9YTLpAn17WxuKjfH5FY6d6mHv4Q66t8ca+l1sw8BMJko/W9ksxtQUVjaLY9uY6TTG\n9DQtao5TjzWxf08Ee/IB9tAdxM5upK5tdY9R8R0qCkjR6Kq+WxVVQlElgiGNptYA4agrSEMRnVhL\noCS0RNGdl1wrkZiv1AURilQ3EyseV1EkQpHqwlCUBCIxH5ouEwhuroOtf8nji6KAfxV5qLUeS9Xk\nuu+5pdXdIittEoiSsBBfs/i6rzUXVlYkguHa5+cL1M9KDYY1rx3Xw8Pjc4X3iebh4fFzQ8bM4jgO\nlm2VKrlLzWxm5pINR/TYjs2d+BB/c/uH/OnIn3E+cQFZkDkQ3Idl2NgZgYDid2d957IV84OqpCAv\nmQ32yRqi4H4k12uZTCZypfbIYgvylq4wkZiP9q4w0xMpZqZWV6HNpgsM3pzCH1Tp39tGKpHn47Mj\nVW+bSRVIL6vW2rbDzU/GEUWBnfvLTZW29TXRvT3G7FSat350qyx/d2IswcRogtaO6sZTRWLNfp74\nwg4c22HozgyKKrH3UJXYpxr09DWz/2hnQ2LSsW3MuTlYJs6LlxcmxrHm57ELbrW9uUlDlgSM8wvV\n3ceeKrufoMjIsRgsETSCAAF/ufCQgiFEeXWibKl4EQQBTVcIR31VTYCKrc2rJRLzlVUbBUEgEtMR\npaUVTKms9VrTK4WhIAhEY/6SmPIHtU1zm3ZFZ/nvqq+xyqv7lDLx6a8jGv2ByuNIkohcY/Y6GNII\nhnWa24JEm91q/XpMnHx+FVWr3JDwBVSCIY1QRK85B67pMj6/F6Xj4eHx+cLrWfHw8Pi5YWn1NllI\nEVD85JcI3qyRI5dzBa+q124nzJt5/tfN/4/J7DQAMTnK7uBudgV24pfcyq1m+8lmCq7hVI2ZOr/i\nI7GQteuXa8+hLscoWFiWKxrHR4uC1zU+2n2gnYnRBLeujPPkc4076t/4ZBzbdth7uIOeHU3MTKa4\nNzBDe1eY7u2u4ZJjO9y+NsmVi6PYlsPW3hh7Dm0h2uRn5O4s6VSBvj2tFaJKEASOPdmDbTuMDsX5\n6d9eY++RDnbtby8ZTx16bGtdMdKxNcLRJ7Zx8YNh9h/t3DSxZCXmccwVHG+rbEjY0xNYd28jbulC\n7OpxLxQFpGAQKRBEEAQc08RKulE6qiLi0yUkUSCZNkCWkQLVo6dWYrVRLoGQRiFvNZwNG47qVZ9n\nUXTbpeMzmZqt1/6Aim3ZpZnfSKxSaIUiOnMz6Zpt9mvFXyX/tFjlXa2xk79KJToU0bGsTNmMffEY\ntcSqpsuYy44tK1KZMN8oE6dQRGduOlPqqCiKXVjYsIj6mJtJl22uiZJQ1c3dw8PD41FnXauFh+14\n6OHh4bFWLNsiZy1WFgtWgXRucZ7VcRxyVh4cyCYNHAc0X+XC03Ec/s/dnzKZnWa7r5cj4cO0qW1l\ni31d9qFISl1nVp+kkxRSSKKEIq3u47jYejwxOo/PrxCOugvV1i0hos1+RofiNeOIlpNJFRi8NU0g\npNHT11yavX3t765z4f1hmloCCA68+aObzEym3SpQROH+vTnu35ujoztCcj6HIMCuGpE5iirx5HN9\n3L83x8dnh7l6YYzBG1NkM0Zd46ml7NjdSveOpnWJAse2sQt5nHwBJBFBVhBlBUGWsDIZrExlW3s9\nSrO7j51GEARETUOKRBDlxddVCgaxs1kc00RbqMCpqkhYVMhpEVYcfK2Cokqrbj0VBIFQVCc+k1nx\ndsVM55U2FWRZIhLzIytizc2KQEjDsmx0n4KiVhPORfG18vmshpVEpy+gkkkbZesXURKQJLGqqdTy\n6m4RQRAIR30U8uaiaHQcZEWq+VyoulwhtoMNtOSvBVEUCUV05ueyZWK3iCSLhCKuCVeRcMS3rjlv\nDw8Pj88q6xK8X/7ylzfqPDw8PDw2lYyZrajMzaUSaLhCK28VoGhE5UAuaWCbNr5Q+cL5gwfnuT0/\nSKfWyYstL5TakEsIIiG1sUqdIAj4FV+ZY/RqmJvJUMhb9O5cnPsUBIHd+9s5+/Zdbl2d5NiT9WdJ\nr19+gGM77DvcUVrwhiI6R09289F7Q7zz09tkMwaWabO1N8bRJ1zjp/HRBDcuP+DBiGuS1dPXTCC4\n8gJ+a2+Mto4Qlz+6z73bM67x1LH6xlNLWYvYdWwbK53GyeexjULVKi1rXOzbM1NYgzcR2zoQu3cg\niBJyU1OF8BEEASkcwY7PoiqL7xs9GsIXjTI/m60641yL1VZ3iyiKtGKEjT+g4g821v5bz1jJbX9e\neTNDVtx26KWt7uvBV6WleOn5+AMKmbSBpssLQtz9HbKZQsUmVbVKcRFJElfV/ivLEpIklqrrmi6v\n2phqNaiaTCTmq7lpoeky/oBKJl0gENI29Vw8PDw8Pk3qflu+8sorfO973yORSOA4rhGLIAi8+eab\nfPOb33wY5+jh4eFRgWmbxPMJmvRopeisQsaorCAlMmmaZA1JlMiZ7mLbcRzemn0bWZA5HjmObfvx\nhxUEQeDu3DDvjJ3BL/l5seWLVY8bUgMNC1jHdhi9mcTnV+jpW71rbTGOqNjOXKSrN4b/wij3BqbZ\nd6RjxXnAdDLPvdvTBMMa3Tuayq7r6W9mfDTB/Xtz6D6Zx5/qZWvvoulTx9YIWxZmhkeH4+w+sKWh\n81Y1mcdO95bih+qJ5PXiWBbm3Cx2oc589jKxaaeSGGdex0mnwDRwTANMEyQZsaUNsXULYks75pUL\nwJLqbqC2s7Kka2jRALBwLqKAHGtCkCSiTX5mV+GOvVbBC+78rC+gYhQsDMMqGZQFQtpDyV9dju5T\nEAQqzN1WiyDUru4W8QXUqqLY51eRFYlE3O380HR5wzOPVV0u5V83Yri27uPVafsPhDQEYWVh7+Hh\n4fGoU/fb8r/8l//Cd7/7XTo7V7cD7+Hh4dEIlmVTyJsLC97GKmy2YzOZmca0TQpWgVZ/C4ooY1l2\n1fZD0zbdCu7Sx7AcrIJNhiwBxU9u4fp72SFupG8CcDszwOORxzhg7sdU8/zg7o8QgJdaXsAnVQpU\nWZLxy40JV9OwOPv23VJ11DRs+va0VdyukDe5cGYY3adw+ET5nOv4aAJBgLaOUNl9RFFg1/52Pj47\nwvuv3+GZl3ZWddWFhequA/uOdFa0MwqCwGOne2jrDLH3QAf5QuVcqyAItG4J0bolVHFdPZrbgqu+\nz2pxTAtjdmblmdwq2Ml58n/7v3ASC+G1sowgKwiKgp1KYM1NY92+Vrq90NKO2NMPAki+lSv8wS2t\nWJMPwHaQYzEEyX1tJFkkENIqDMGqsZZ25uUIgoCqyZs2C71aNF0h2iySmFtdpbuIJLlzxfXaclf6\nnFEUiVhzgOR8blM2YrQFwevzV2+V/jTwb/KGk4eHh8enTd1vuZ6eHh5//PGHcS4eHh4/J1imTT5n\nkM+ZJUMno2CVZVymjQx+ubJS5jgOU9kZTNsVMKZtMpGepElrIjtvEgxV5oSml5hVmbaJJEiYBfe4\nGSOLLMrg2DiOw4XERQCOho9wNXmVd+fe41rqOiIiOTvH6dgptmhbQABZlJEFGUmUkAQJVWqsDTSb\nLvDezwaIz2Zp3RIkEc9x8YMRRFFk+66W0u2SiRzvvTZQarPUdJm9h11n4nzOZHY6TXNrsKpg6dvd\nysxUmpHBWd57/Q5PfbG/olo1M5VmaGCGUESnu7d6XI+sSOzY1YrPr1YVvJ9lbMPAnJnFsStnMwWz\ngDI5jBVrx/KVi3U7EXfFbnKe8FPPEHn2CwhLqvkFwyJ+fxJ7agJ7ahwnPot89KRb3dV1hBUqpLIi\nougqQjSGnU4hh8qdqX1+hXzWqGl0VmQ91d3PMooiEW32k4hnywyhRElAUSQMw6pqcKWornHWRsyg\niqIbnbQZKIqELIueyPTw8PB4iNT9xjx69Cj/+T//Z06cOIEkLX6JP/nkk6s+2F//9V/zgx/8oPTz\nlStXOHDgAJlMBr/fnfH51//6X3PgwAH+x//4H/zoRz9CEAT+xb/4Fzz77LMkk0l+8zd/k2Qyid/v\n5/d+7/eIRqOrPg8PD49PD8Owqhrm5HMmqWSeYEjDdmxmcrPMCzJNegxdXlwczubmyJvlFTDLtrg3\nPkZQDCEKlRmYWdM9XtrI8CfX/oL2QCtfankJAMexSeRd59zR/ChThSl2+LZzMnqCQ6GDnI2fK1V8\n+/19HAjuB0CXdKL6YivxxFiCm1dGOHR8K9EVDJjiMxne+9kA2Yw4uXsdAAAgAElEQVTB9l0tHH1i\nG8n5HG/96Bbn3x9CEAV6+5uZGk9y5o07FPIW/XvbGBuOc/XiGOGoTldPjMmxBDiwZWv1KB9BFHj8\nqV4s02ZsOM4Hbw7y5PN9iKKAaVhc+/gBt69N4Dhw4FhX1fzb9bCSEdBGY+dz+JwciBKOKIIo4ziQ\nmpmt2qZsX/kI4+pFnHweRBF1937EQycRm1ux52fJ/83/wkkniTz7HJGnn604nqpIhDvbSEWaoH9v\n2XWSP4AoCoiSWDXHuNhuK4VCiL5KUSUIAsFIfVOpz6vgBbdSG23yk8sabpyPIpUJ2XzOJJsplN5b\nPr+y0Jr7aBguhRuoQnt4eHh4bBx1vzHff/99AC5evFi6TBCENQner3/963z9618H4Ny5c/zwhz9k\nYGCA//gf/yO7du0q3W5kZIRXX32Vv/iLvyCVSvGtb32Lp556ij/5kz/hxIkT/NN/+k/5y7/8S773\nve/xW7/1W6s+Dw8Pj08H23bKXEGXk00XkGURR7HAAdMxmcxMEVKDRLQwiUKSdJVZ3GzSxCrYzDNP\nxsggaBaRYAhBEDAsg8JC9M8bI++QNFIk4yk6xWvsC7pixVkwq7ow/zEARyNHAPBJPr7Q/Cx7g3sZ\nzY1yMHSgtKgOKIuiNpXM88EbgxiGxVvTt3j6pZ00tVS2td6/N8eH797DMm0OPtbFrv3tC6Y+Pp75\n0k7e+tEtPnr3HrNTae7engbH4fipHrbvaqF3ZzNvvHqTc+/c47mgVhFHVA1RFDj57Hbe/9kdHtyf\n59zbd+npb+bjD4ZJpwoEQirHnuyhvbN2/u1a8QdUFEXaUPfdali5HEp2HtUng23BksKoElJIZ0wK\nho09M4Xx8Vms21fBthEDAYJHj5EduE3h+idw/RPEnj6cqQmcTIro8y8QPvVUzePqmoRpOuTyi6JW\nUGTUgK8kaOKz2QrRW4ygEQQBQak+a6ooEj6/Uorzqbh+A9qZP+sIglDTEErTZTfix7SwTLsib/ez\nzmelldnDw8Pj54W6gvfP/uzPNuXA//W//lf+03/6T/zLf/kvK647e/YsTz/9NKqq0tTURFdXFwMD\nA5w5c4b/8B/+AwDPPfcc//yf//NNOTcPD4/NIZXI1c3bTM7ncPzl87bJQoqMmcWq0pqaS5sYucXL\nDctgdGaatJAmpAZL9xlKjHB19iatvhYS+SRn5j5gq76VsOy2s47nJxjLj9Gtb6VVbS07RrvWRru2\nOF+rSAqK5C6yLdPmgzfuYBgW2/qaGB6c5e0f3+KpF3bS0h4s3ebSuREGb00jySJPfGFHmfkTQLTJ\nzzMv7eTtH99m8ObUQozPDto6wqXrTzzdy5k3Bnnv9YGSqU60aeXWS0kSefL5Pt796e1SjJAguHm9\ne490Im+wKQ+46TrFmeyVhNt6sXI5zLlZwpHqwkgUQZ0eIfP+exTu3gFAbmom/OQpAgcPIcgK0S++\nSPb2LRLvv0dhyL1N9MUvET65wqauADgQ8EuYlo1pLmSdRsNEm/2lTZFIzMf83GJWq6bLDVf2AiGN\nfM6sOsv6ea7urgZZlj4Vgy0PDw8Pj0eLmt+a3/3ud/nOd77Dt771raptQn/+53++5oNevnyZjo4O\nWlvdReXv//7vMzc3R19fH7/zO7/D9PQ0TU2LbqFNTU1MTU2VXd7c3Mzk5OSaz8HDw2NzsG27avUp\nky6QzzU2Azozk0SPiGUzp9XEbiFnkU9XPqZt2uQyBSx7vnTfnwy/CcAv9n6RsdlJXpt4g7dm3uaX\n2r6MIAhcLM3uHq17fgFlsXp78eww8dks23e2cPx0Dx1bI5x7+y7v/PQ2p7/Yj6bLnH1rkEQ8RyTm\n4+Sz28tmlZcSawnw9Jd2cuf6FLsPbill6xbp6omx/2gnVy+OAdDTtxh9I0ruf6ttKMiyyOkv9vP+\nG3ewLYejJ7tXbLteL7p/cZbZH9TIZc0157a7j+Ow/O5FsaurUoWIdByHzLWrJM68hzH+AACtexuh\nJ07h27WrbB5XEET8u/bg37WH/P0RnEIBfUdf9ZMRBZSmZgRFwZicBMsiHFSIJwxUTSLaWR5F5M6C\n+pmfzWCadl334OW/dzCsV+2I8ASvh4eHh4dH49T81vza174GwG/8xm9UXLfeOZnvf//7fPWrXwXg\nV3/1V9m9ezfbtm3j3/27f1dVSFdbKK1m8dTaunr3UI/PFt5r+GhgmTaT40lUn0gwpKGo7keMUTBR\nZQklWl9kWY5NWpTBBEWR8YXkihZAs2Dz4fAnXJi8jGVbODjYjoODw/ZgD6faTqDKCtGYKxjfuPs+\ns7k5nuw+xr6t29kitzOYuctg8h6D1gCd/g6GssN0+TvY1dq74mecJMpsCbrV2VtXx7l3e4bmtiDP\nfmk3siwSPeonFNJ5/dUbvPfaAAju87L3UAcnn9letyIVjfrZ0d9a8/onntlBNm0weGuK/j1tRBee\n03BER1akFWNt/sE/PrLisesRbeD1EwRo2xIu26wIBjTm52q3sld/HIFgSFuodBplrdFmJoORykJI\npzmmlVWpjWSSkb/4K5I3boIgEDl0kNYvPEugp34eMbHdtc9HUdC3tCNp7jy53R4hNz6Bnc8TjTro\nzTG0luqeEq0tQeZmMzS3rt6VOt8axLIcHNvBth0E0X1e1oL3Ofro472Gjz7ea/ho471+jyY1Be+e\nPXsAOHHiBOl0mvl5t1JSKBT4V//qX/H9739/zQc9e/Ys3/nOdwB48cUXS5c///zzvPrqq5w8eZK7\nd++WLp+YmKCtrY22tjampqYIhUKlyxphaiq55nP1+PRpbQ15r+EjQiqRc9tX59yfFVXC51dRZYm5\nucZmOTNmljvzw1xP32BfcC+tWiuqLqEFZGzLIZ3M8ebEu9xI3wBARHTnIRFwHIfJ3BQ34wM81/wF\nes0uMkKKn915j4Ds57HoY4wOzWPkLE6HTnM/NcbZm1foGDPZJhxnS7CNy6MTyKpIIKIQbdeRpHLx\nG9SCxM0M8ZkM774+gKJKnHi6l1RqMT802uLnyef7OPP6HSRJ5MRzO+jqiZFK1Y+baYQjT3SzdXuM\naIufeNx9XkXFNYlKZ/KbYhQVjS4eayU0XWZ2rlJ0J1M5rDrOw0V0n4I/qJIrGORm3HboTCZHZi6J\nnU6XIoZURSQpL25+Zm/fYubv/gY7k0Hf0UfsF76M0tRMAShUef+Juoadq/+aiD4fSihCJlEAFtvt\nHTWEkcxjp7PkQk2IdT6nNupzLJsr1L/RMrzP0Ucf7zV89PFew0cb7/X7bLPSZkTdvqjvfe97/Lf/\n9t8oFAr4/X7y+Txf+cpX1nwyExMTBAIBVFXFcRz+yT/5J/z+7/8+4XCYs2fPsnPnzv+fvfsKkuM8\nD73/77fD9OS0AcAiJyIQBDMpilEixaRAyTIt6Vi2TsnlUtlS+cJRsi/kK8ulksvlKl34++rI1qfj\nIFs6sikdiRRFgRQjGEAxAQQIIhDAptmdHDq/30UvBljsLgCCJIgl31/VFmpnunt6pncX88zzvM/D\ntddeyz/90z/xla98hVqtxuTkJOvXr+eDH/wg999/P3/wB3/Az3/+c2644YZzPg9FUd5eURTNWavp\neyG+1zurzOBxXuCxs/40x9xR9rRfZW1yDVcVrqToFKl7dR6c+gXTfpWyWeYjA7eSN080bfIjn531\nZ3i5/TL/NfHfXOZeSj2qEsiQDw18AL8JyDgYTBtpritcx5EXA8xeFpMs3Tp0afePJ3SN4nCC0kiS\nXMmi2w6ptHzqU6NMV9pEoeTam1eTnifjtnR5nts/uRXD1N/2ElQhtFmzd63EiSx4Jpt4xxtFnc5C\njYayuQT16umzvKalk8kmZs0MllISNhpYrQadlos8aU1rMhlvF/k+9YcepP3s06DrFG67nezV18wq\nXT6ZZhoYpTJ6MknY7eJPVeZ0cwZAaBj5AkZ+/sZgmhBYg0OEqQ5igQZUiqIoiqK8u874LuyBBx7g\niSee4Itf/CLf+973eOihhxgdHT3nB6xUKv11uJqmce+99/KFL3yBZDLJ8PAwX/nKV0gmk9x77738\n9m//Npqm8fWvfx0hBJ///Of50z/9Uz73uc+Ry+X45je/ec7noSjK26vXeXsaE1XdGsfcUYpGAVOY\nHOgd5GDvEGuSqzniHMWXPlsym7mu+AEMbfafMFOYXF+6jjWp1Tw8/Qi76vG63BF7hDXmWjglpik3\nljPVq1IvH2PjZQOsNFcReBG+G9GouEyP9pgedZgedThVOmtx8eXDLFux8Gi0+QLhd8LJa0MNU8dO\nmji9c7secbMpzqnRlGHqmNb8JdumZSx4XkJoZHKJOd12ZRThT04SOQ4akE0bNFrx/qYh0AOX5jO7\naD2zk7DZxBwYpPzJ38AaXjL/CWpg5Avo+Xy/bF1PpdCWLsWfrCD9E+cmkjZGqXxWgayentuRW1EU\nRVGUC4Mmz7AY9nd/93f57ne/y2c+8xn+/d//HYAvfOEL/PM///P5OL+3hSo/WNxUCcmFL4ok1Up7\nTmOh4862HNYPfX458TBP1p/i+uIH2ZrZwqHeYZ5pPEPVr2FoBjeVbmRDev28+08d7ZLKmaRyJn7k\n82R9J8ecY9w5eDsFc3ZgKqXk+V9M0m34LLkpYt3AihNrd49XMUvoNn2qoz3aDZ/BUp7yUIZiOX3B\nNA4SQqM0mJ617jiKIqqVzoLXYyFWQidXSKJpGq2GMys4PZtrmCvYZxwRE0WSwA8J/BDfj9ANQTpj\nzVk3LcMQf3KCyJ1dvtvpBnQmpxF7dtF76Xmk56GZJpkrriJ/0y39AFWkUgjbBg00NNA0tERiwQBW\nRhF+pYL0PIxS6T0XxKq/o4ufuoaLn7qGi5u6fhe2t1TSnM/nue+++9i4cSNf/epXWbduneqOrCjK\nLE7Xe9PB1Xzc0GN/dz8aGutSa9E0jTWp1axKruSIc5SCUSBvzj8zdnR/mwO/rmMmBJfdOoyVNLmx\ntPAc1eljPboNn8GVKdYPlmbdl7Wy2HoCN/JIGB6pvIWlW5TshbO575bEzPifkwkhSGUSdFpnv2bY\nMEQ/2AXI5uOGX/NlZDVNQzc0wkD2GwgKXcNKnPlDACHi7U63rQwCvImJWRlXgKBWo/foIzgvvQBS\nomezZK+/kcxlVyCSJzpfa4aOOTCA9iZm1WpCYA0PI6PoTe2nKIqiKMqF7YzvTv72b/+W6elpbrvt\nNr773e8yPj7O3/3d352Pc1MUZRGQUtLpuDiBhxd6RFKSsdIYYv7S1khGSCnRZ+6PIsmBvRUy2QSt\nTIWKN8UKewVJ/UQAIzTBquTCXXZr4w4HXqgjhIbvRux7tsbW68sLdluWUvLG7iYAKzfP/kTQNmzS\nZrzmOCWSpIxk/5zfCULXQDLvvNX+NiJuyDXfKSQXGHWTTJk4XZ8wPHOjKCE0csXknNcrm7eRUvbH\nSemGIJW2SNhGf1spZb8Z1Vvt4A8QeR7+5AQyONF4K2g0aD7+K9q/fh6iCHNwkNx115PashVNn/vf\nmFEqn3PQqoJdRVEURXlvOWPA+73vfY/f//3fB+BLX/rSO35CiqJcuAI/xDB1pJR4kY8TONSbbZrt\n7qz1sU7Yw9ZtMlamH/gGYUA36NENHISmUbaLON2AnY8cpFqJu/pqRZfk0jwbyvOXLM+n2/TZu7OK\n0DRuumMjr744ztjRBqOvdRjZeMoYGA1s3ebY4TrdZsDQqhTJ7ImAUQidXGJuSYzQxIky57eREBqF\nYgo0aNYdAn9ud2XT0skVbKJQ0qj1ZgXGpqXPGv9zMk3TyBXsOfvM3Q7yxeSc0U/HZfM2uu5RHsxg\n2nM/xNA0bVaTqTdDhiFht4P0fKTvEXnenOZRjccfpfGrhyEMMUpl8jfeHAe6CwSmIp1CT71zM4YV\nRVEURVlczhjw7tu3j8OHD7Nq1arzcT6Kolygel2PqWoDT3qERoBhxWWtrbY3pxkUEpzAwQkdbN0m\n6PSY6jX7d0cSXjswyt6np/G9kOWri3iez+QorKt9kKCRoLPFx07rCF1bMHPouyF7nqgS+BFX37iG\n8lCGK69fzYP37ebQS3UKgzbpotEPdDNmGl3o7NxzFE2DFZtPKo/WoJDIx8HteaBpkCsm+wFroZSk\n3XRnlRAn01Z/fasQUCilqFe7/QDWXiC7e5xh6hTKKRrV3ryZXl0XZGfm9y58nhrpbCJes/w2Ll0K\nWy2Ceg15mgx067lnaOx4CD2bI3/zLaS3XYK2QOUAAELDLJYWvl9RFEVRlPedMwa8e/fu5a677qJQ\nKGCaJlJKHMdh586d5+P8FEW5APheSL3epe7E87hxwe2AJrRZY2LmmAl8zZMSl1EkOfxSg2OvtRG6\nxuUfWMmajQPsa+7nqT1PsfrYdqrHoHpsor+PbmoYpsBK6v2mVNm8zdE9TXptn83bl7JybRzoJGyD\nq29Yw68e2MfenVWuvWMlueSJTPORg1VadYdV60oMlPJ0vDi7nLEyWPr5Gy2TKyQxTwo0NU2Ls6mG\noNt2yebnNoDSDdEPYKNInlXjLF2f2afWm5VBTqUtUhkL6brISFswYxp2u4SNOkFy5Byf6WyR7xFM\nT59x/m1v/z5q9/8UkU4z/Dv/E6NYPOOxjWIRzbgwmokpiqIoinJhOOM7g6GhIf7xH/8RKSWaFq8j\n+9SnPnU+zk1RlAtAFEU06z3aM4HhyU4b7J66rZRUjvR4Y3cTpx2QzBpsuqZEaSDOYO5u7aGbq7Ji\njUmhMURj1Mef6ebreQG+F9KqerSmZ3ftXb66yJZLl866bWhplk2XLOHVF8fZ81SFwaUOvY5Hr+tT\nGW+habB5+zIyVgJTmDiBQ8Y8f115s3l7waZNqbRFMjW3EdVxcQCbxHWCs14zK4RGoZSkWXeIoohs\nLs7qyjDEm5xA0wR6IY+eyZ5YmxsE+LUqUSfuzOyMT+A50ZxRPcfLkqOeg7AsRDKJSMwexySjiMhx\niJweYas1tyLgFN74GFM//E80XWfw3s+eVbAr7ARGdv6GZoqiKIqivH8tGPDed999fPvb32ZsbIzP\nfe5z/duDIGDp0qUL7aYoynuIlJJm3cEPApywd8btK0e6TBzskM6bZEoW2ZJFIqUzfqjN3men6DYD\nNA2Wrkuzelse3RA4gUO1F/Fa5zUSIsHK1AoGygMkNlpzjh+GEe2mS6vh0Gw4hEHElu1L5w38tly6\njMp4i7GjDcaONvq3x8HuUjK5OCizjQS28dbn5QqhkS8mkUAURgRB1G/mFD+uhqadmJN7OmcKZIUQ\nJFNzX58zHTNfTM66LWg2IZJIQoLpKmGziZEvIGVEUKvNWU8b9Ry80WMY+TyaYRJ2OkROrx/ARt0u\n1Otouo5I2iAEkeMgvfln+sZBsIOey/UzzEGjQeX7/4r0fQY+fS+JkeWzn4euo2ez8YWUEpDISKJn\nM/M8gqIoiqIo73cLBrwf//jHufvuu/nLv/xLvvKVr/RvF0IwNDR0Xk5OUZQTfC+k1XDIFW0M49ya\nBAGEQYTnBViWsWDDo+M6bQ/fC+n43TNm5ZrTLvueqSIjqE+eKFcVukYUxjsPr06hrW5xRHsJy1/L\ncn0ETdM40DlIN+yyObMJ20ySMOYP5nRdkC8m5wRu8xFC4wO3rGP0jToJ2ySZNkmmLGzbQBNvfweq\nXCF5Yi2sqfPWQ+h3lgxDwlZz9m1+gD81dYYdIag3Tr9JGBK251YEAIS9Lr29e+nueQXn4AGIItB1\njEIRs1TCn54mbLUo3PoRUpu29PfTTAMjl0dkMm9LN2hFURRFUd4fTlvSrOs63/jGN87XuSiKsoBu\nx+vPVG3WHYrl1Jt60x/4Ia4b4DrBSVlHFzMhMFIQEmIKg9TMOB7fD3F7Pr2uTxiFdIP5s7tSSia9\nSQ7WjuDuLCEikzc2PMdQaoD14RbcuqRT9ykM2QyvT/FSuIsXWi8C8GrnVbJ6ls2ZTUx5cZC1IbWe\njPH2ddi1kyZrLxo85/01TcO0RLzWVtPott15RwNl8zamde4fQrwbgkZjTgb3VFJK3MOH6L66h+Qt\nN8A8HazPljcxTv2XvzgR5ALmkqWYpTJBvYpfrRJMxz8HmSuvInvNB+IdhYZZKiPSaRXoKoqiKIry\npqnuHopyAYsiSavh4LlB/7YwiMt6s3n7jPtLKed0/gXoBQ4tr03UDqEKlq1jJAQ5kUcExqwxNr3A\nASmRUjLhTVD1qlT92szXNG7gs2bPtaR8C3fdGOklgtfd3YyKg9y08kY2pVahJQP+69BPGXPHyRt5\nri5cxRu9N3i9e4CnG88AkNbTrEyvIvE2lBe/VXYyzgifmklPJAyajR6Bf6JUOZkyz1iifKGRQUDY\njrsk13f8Emt4GHv9BszBoX6vBufgAZqPPoJ75A0A9r3wPMXb7iB92eVvKvCUUUjzicfj0UJRhLV0\nKanNW0lu2oJZOtFRWUpJ1OsROT3MUrl/u1ksoWdUubKiKIqiKOdGBbyKcoEKwygeQRPOzcI5PR/T\n0rFsHS/0512D6nsBzYYzZ38pJW2vQxTNdOyV4PVCvF5IV5ummCj0S4ojGcXlzMCO6sPs67w261g5\nPcvaI1cjOlkGVia56NIrAXix9RI7609z/9QDrEutZcwdoxv2WJtay82lG7GExbrUWq4rXsf+zn72\nd1/novQGsuexcdRC0tkEqfQCJdWGoFBK0W17dDseVkInkzvzBw8XmqAZZ3drDz5Ab++rdF95CX75\nC/RsFnvtevypCt6xowDYGzaSXLee5iM7qP70xzgHX6d098cQdhIZhjgHXqfz0os4hw9iLVlKcsNG\nkus3YhQK+FMVpu/7Ed7oKHo2S+mjHye5bsO856RpGnpq9gxdkUrF63UVRVEURVHOkQp4FeUCFEVR\nPHpmnmD3uHbTgdDHw2NJeqg/P1ZKSafl0uvO3yioFzqEUTDvfUhJzW1Q1gqYukkvcJAy4o3eESYP\nd1hfvY58Kk06ZZPLpPA7kmMTbbIli41XlPqZv+25S1hhL+eh6R283j2AQPDB4nVcnNk6KzuYEBZb\ns1vYmt2CaVjvanZX0+J1uAt1Tz6xXTyX1kqceQ30hUgGAWGrhTt6jN7eV7FGRsheeTW9/ftxDuyn\n88LzACQv2kT++huxli4DYMmV2znw3X+hu2c37ugoyXXr6b66O25URRycOq/vx3l9PzV+ijk4iF+t\nQhiS2nYJpY/ciUieee31cZquY5bLZ95QURRFURTlNFTAqyjngesEWAn9rEpBpZQ0aj3CMDrtdo7v\n0ZhqkM6bTLcapLRUf4zPfOtMj+t43Tm3+ZGPG7lkjAzIiKpTp2QX6fhdvNDnxRfeYPnRS+PnUgeX\nkCotAKykzubrygh99nMrWSU+teQeXm3vZXVphLSfP+3zeTvW7iZTJlEkcZ0FAvoF6Lp4083AFtua\n3eOCRgMkNHY8BEDhlluxV68hvW07MorwxscQiQRmeWDWflaxyNDnf5fGo7+i+egjtHc9i0inyV59\nDamLL8Fauoyw2aS3fx+91/bhHDyAsG1Kd36U1KbNc09EI/6UYYF1xMbAAJq+OF9jRVEURVEuHCrg\nVZR3mO8FTE7VyGcz5PKnz3AdD3ZPXiM6n0hGNL0WURTRmnZpaR4DSQ1DzA4QpJRMjrWYHG0SBBGu\n59NzXaJQUhhOkFjusae3h32d1whkwC3lm9mY3oCUEdNOFRlGPLPzAIXRVWAHXHbDCLqu4fZC3F6I\n74SUR5JY9vyBia7pbM1uIZu0afnOnPuF0DGEgaWbZ8zuCqHNWlt8Kjtp9suLXcen3XRPu/3J+6Wz\nCcQ70Ln5nSCDgKDZJHIczMHBWTNxF9wniohcl8hxCNstnEMHcQ4ewF67Dnv1mv52mhAklo0seBxN\n6BRuuoXU5i1EnQ6JVavQTvqZM/J5sldcRfaKq5CBD0LvjxtCaFjDS9BM88RtQOR7hM0WYafdD371\nXBb9TWSDFUVRFEVRFqICXkV5B0VRxNR0i7rbwAlcDGNowfWhAK2Gg++FJ/aXEUITSCk5drjO3pfH\nWbGmxPC65OyyZClpei1KdmHmW8n40SZ7XhijOjX/eJjauIO/u8fkSAtz2ELTNH45vYNABmzJbCb0\nQ156Yhw5mcZNt/jATWtIpeLgys68hT8dGuQSOSxhzQnQT2ZaOoapY5o6piUQQuC5Aa2GMyeQTdjG\nrCZeCdvEtHTaTXfBbK+uC7L5BKa1OP4MRp5H2GwQdjr9EVH++Djm8DDCmvszJaOIsNWKG0G5Tn8f\nKSX1mexu/uYPndO5WEPDZ9xGM04KxIWGNTSESMz9UEOYFqJcxigWCVtxIG8Uiud0XoqiKIqiKKda\nHO/0FGURklJSr3Wp9eISUidwmKzWWGaU56wTDYKQdtOdFex6oU/VqRG1Bfuen2ZqvA1AbarL0lqG\ntdvjEuEIia4JvMCl5ztURx32vDhGfTouXV62ssC6TYNopqQbtTnsHubR6mOUx1czMLmGkUPbSEzq\n5FZp7G2+xmuHpmmK1zHaaZx2RCs/yaZry6RSZ9mcSRMgF85QZ60sKWNu9k4IDSthYCV0rIQxb/m3\nlTAoDqRoNdx+52orYczbsVoIQa6QxPdCgiAkDCKCICIMI2zbJJWxLvgxN3Hn4u5M4Do3Qy7DEG98\nDGtoGGGfeA3CToegVkUG4Zx9eq/txTt2lOSmzQtmczVdR0skEAkrDlKFjgi7wNxy+DPSwBwYQNin\nz9hqQmDkC3D6yndFURRFUZQ3RQW8ivIO6bQ9plt1wihASommabTcFpPTOkuGihiGThRFdNvevA2m\nqu0G+1+oMn4gztAOjqTZdPFSnnvyMGP727g9n8NrdjERTHDX4B0kmnleeOk1WlUPgOWri2y6ZAmF\nUrw2dqo3TeA6PF59FBIR11+9lXxY5MjeFuMH2lT2QIm4vDUAAgKqg29gb+qwJnv5aZ+roRvYhk1S\nt5FSUnXrJ7pAn8Q2bNLm3LW6mVyCZGrhzPfJhBDki0l6XU2IwxwAACAASURBVA/PDckV7NMGrqal\nX7DrbSPfR7ouMoriIFPXQY/XeoftNmG7NW/QOvsgEm9yAnNgEE3XCWpVIsedd1MpIxo7fgmaRuGm\nWwAwhwYRCTteTwugafO+nslyEdHy+k2qTqbpApHJIl1nzmOb5QH01LvffVtRFEVRlPcnFfAqyjvA\ndXxqjSZO4NCouOx+YorB5SnWXlag1mtiTBnkMkm6HW/eBlOHD1X49ZNH8d2IZNZg7fYCxSU2UnO5\n5OZBXnmiQvWYi2guRSxvsuvVY2TqcaA7vCLDtstXUCieCCydwMULPB6a3oEnPW4s3cCgFTclWndp\ngeUbM7SqHoYlcPUeO5q/pKHVMHSd3yrf2z+OaVjoCIQm0DQNoQkSwsLQZ/8pKSUKc4JeXRjkEnNH\nzGTz9jnNsU2mLJJvvc/VeRe224S9LtJxkeEZgtlTyDDEPXYU5/X9+JVJEqvXkN6yFT2Txa9M9suW\nF9J9+WX8yiTpS7ZjDg4hkvZZB6OaEFhDQwT1GkG9Ed+mC/RcDj2b66/LlUFA2O0SddqIVFrN0FUU\nRVEU5V2lAl5FmXG85DVhn/uvRRhGOF2fdrtH3W3hdANefWqa0JeMH+zgdAM2XVtmWqtBBPopa1h9\nL+SFp49waP80moDVF+dYtjF7oqGSlGBEHNn0HNruAfLVZazd8wEAutkamy8dZsVwAYcWk90uuibQ\nNQM/8nmh9SJj7hirk6vZnN4063ETKYNE6vjztvlo/g4eqz3OhvR6MkYcEKXM1LwB63wM3ZgV9Gqa\noJDI9UcnHXeuwe5iFXY7+FNTs26TQQDI2WteT74/Cum+8jLdPbtxDh1Eel7/vt6+vdQffAB79RpS\nWy+ORwhJiYwiiCKk5+FNjOONjeGNjxFUp0EI8jfeDHBOa2WNQhHNtJC+h57Lz2pABaAZBkYuB7nc\nmz62oiiKoijK200FvIoCBH5Io9YjiiTJtEX6Ta7v9L2QXtfDdeLy5ZrTIAxC9jwxhe9GjK94lVx7\nECbKvLhjkq3XD1ClTskuoAu93035uccP0+14pAsmq65I0bEbdCKNjJZB0zSCKOBnUw9wzDvGqosF\nIxMbaE75iNVNXhZPcsxP8DHvbgasAaIoJCLEx6fiVXim/gwpPcVNpRtnPzcNUkYKQzdoui2QkoyR\n5o7Bj/Q3sQ37rIJdK6FjWgZCaPFc28hmyqmyvDxAuxrguScymrmCTcJ+/wS7ke/NCnaljGjveo76\nQw+CppG5/AqyV12DkcvP3C/pvbqb+sM7CKbj/YxiCfuSddhr12MNDtHbv4/OKy/hHDyAc/DAaR9f\nSyRIrFpN5oorMQpF9HR63iZSZ0NPpwFVpqwoiqIoyoVPBbzK+57vhTRq3X5pca/jEfjx2lBxSvYK\n4kAk8COCICTwI3w/boh0XMNt4gUerz4zSaceUBs4grd8ijfCIwwe2giTq3j2oVHWbS9xuFunV4uo\nVjr4XoimwcoteQY3WvzX5H/RaMWlo4ZmUDQLRDJi2q+yOrmK2wZuRR86niEewmpH7Kg+zI8n/y+X\n5S4lb+UopHLkkml2TOwgQnLH8lvJ2WlCXxIFEaZhkTOzmDMlyaZmUncbszpAW0aCfGLhbJ1hChK2\niZ005rxeCUxsewlLBgpUZAspZb8x16mNuxYbv1ZDel5c0nuGEToyivAnK/2xO/70FNWf3Id75A00\n20bTdVpPPkFr51Oktl5Mcu16Wk8/iTc2FgfDl11B9gPXYZbKs46bveoaslddQ1Cr0d3zCkGzEWdc\nNQFCoOk65tAQ1pJlGMXiiQ86NDCKqhOyoiiKoijvfYv7HaeivEWeG9Co9ebc7nshtaku2byNlBAG\nIUEQEfhxl9+FNN0WTuDwyu6j1I9qdNM10ltc7hj4NIH0eSH7EqOv7WPojY3sf7re3y+dsVi6Is/I\nhhwi7XN/5QEaQYPVyVUYmkHNr1P1aoSErEmu5taBD6Nrs8uhL8psRCJ5uPoIT9V3zjm3q4YvY8Pg\naiAupc5oGSJHJ/BPZF1N3aCcLFJ3m3iBi6GbFBK5WRnheFSQ6I8N0vW5Hwqc7OSybU3TzjnQjVwX\nzTDixk7nQAYBYbtF2GojMhnMtxDwBY06YSP+MCLq9QhMAz2TRc9k5j0/f2oK6fvIKKT11JM0fvUw\nMghIXrSJ0h13I5JJOi+/SOupJ+m+9CLdl14EILX1YvI33TIn0D2VUSySu+76sz5/PZtDM9Sff0VR\nFEVR3vvUOx7lfcvp+bQac0e9HBdFct5g+GTHs72O41NvtWi02uyffgP/tRSB6bLsKpOt5ZvQNA0T\ng2uLV+Nc4bCrtIeJiQZhxuH6NVcyUlhCMZFn2qnxdO05DjtvsNwe4SMDt/XXvUYywokcUvoCnZo0\nuGzZxWxctopKb5qm1+p/mcLkxpF4ra9tJCjbpTgQTcdBf6ftEvhxIC80Qcku0PG72HoCoQmErpFK\nW9hJ810b5eNPT4OUWMPDbypYixyHoNkk6nX7TZ3CRgMZ+JjlgTlrUM8kbLXwp6ZoPPorNMMgc/kV\n6Kk0Qa1GUK8hEjYimUSkUgjTJKjXibpdIsdh6v/8J86B1xHpNOVPfJLU5q3942YuvZz09ktxXn8d\n943D8Zrc4SVnPiENhG0j7CQy8Anb7dM3rxIaRl7N/lEURVEU5f1BBbzK+47T8+m2vVmZWtcJ2PPC\nGN2Ox+r1ZZYsz59oFDWj03Y59NoUU5U2vhviOgFuLyCKTo0uMqBFrL06y6qB4TmPb+s2162/jFeG\nd/No7TF+1pzg7sRdhDLgYOcQzzV3kdWz3Fr+cD/Y1YSGbVvkEjaBH+E5IVEgMXQTUxj40ieRFRim\nwKJA0S7M+9zzidyc8uR49q2B74X4fhj/64WkzRS6LkhlLBL2/HNxz5ew3e43a/ImxrGGl5wx6JVS\nEtSqhM1W/za/WqXz611YS5eS2rwVPxjHHBw66wA67HRwx0aZ+sF/4Bx4HYDmY78ivf1Sstd8ALNU\nJnIcIseBWg3NNJB+QFCvUfn+v+JXKtjrN1D++CfRU/EHF3o+DzIibLbQNEFy/QaS6zfMelxN10/M\n2T1+GYToB7onB+16LkdQq887PgjAyOfPOUuuKIqiKIqy2KiAV3lfkFLOZDK9WettpZQc3DfFy7uO\n9Rsqjb5RJ5k2WbtxkFXrykxPtjn42hSTYycCJyHAtHUyhTgYlFbI0fAwNW2alJ3gA6sup5SfafKk\naaSyBpEE3wkJZzKpW7NbMDSDh6uP8OPJn/DB4nU8XnsCQ9O5ffAj2LqNbgqspI6ZEP2AU7cExXwW\nK7KJXA0p4wZQISG9wMEJegQyJDxpJJDQBOVkiaRhL/ga9efVzvQiCoMI3Xhz2c93gowiglrtxPd+\ngDc+hjk8jDDnn90b+T5+pYL0PKSUuIcP0Xr6KXr79va3yV03Rv6WDyHHxzDKA/1y6fkyvjKKiBwH\n59ABJv/tX/DHx7E3bMRes5bWzqdoP/cs7eeeJbnxIpIXbSa5dh16Nov0A9xjR6n8x78RdTpkrrqG\n4m0fQZsp89ZzuX5ptWaaBNXqnOysSKUwy+WzDlKFaWENDRG5LkFzZnyQ0NF0AUJXY4IURVEURXlf\nUQGv8p4UlxqHeN6JjOWpqlMdnn/qDWpTXXRDY80lefKDCcYPdakc7vLK86O88vxof/vcQILh1SnK\ny5LoptYPQNtBh59Vfsa0X2WVvZJbB67DFDPdhzWNVN6MuxcLEytvokuDyNXoOg6b9IvQhc4vp37J\nw9VHALh9ya2sKC/BsOJSYojXwVrCwtIt0mYSQ8z86p7UKFdHYOkm+ZO6KYdRSCijeDyReHNZvQsh\n2AUImw1kGOJPTyGsRBxIBiH++ARGuYxmGjMBXfz8wnYbvzqNDEN6e/bQePxR/IlxAKxly0hvv4zW\nU0/SfOIx/KkK5Xs+hQwmTjyg0E7MlI2ieBSUjBtNTf7r/yZs1Elfejmlu+5GEzrZK6+i++oeWk89\nQW/f3n5QbQ4Nk1i+nM6LLyDDkOLtd5K96pr+w+i5LGap1P/eyObQDDOepxtJEBpmsYSePbtRUKcS\niQTW4NA57asoiqIoivJeoQJe5T0hiuLuv4F/oix3IdOVDntfHGP0SJz9GlqZZuXFWY7KQ0yHPbZe\nuoXVF+eoH/WpjjqkiybFFSakfH5VfZTRiVGEpqPPfDmhgyc9tmS2cH3xunjNq9CxdJNSKUs2lcQU\np6x9TUKRDKUgTb5nk80l+emhn3P50HYuXb4FgIRukUvksIT5poPV43Sho3Nhla9Grkvk9OLGSWdY\nPyuDgKDZxDl0kMl/+f8A4pmz2y4hddFmZHjSddbiTGYUBDgH9lPf8RD++Hj8ocOWrWSvvhZrZDna\nzPdTP/xPevv2MvHd7zB472cx8jNl4JFEnpQdlzKit2cP1Z/9hKjXI3/jzeRuuAkjl4VIEnY7pLdc\nHJdIVyZxDrwefx0+hD85gWZZDH76XpLrN/aPqWez8zai0pNJtKVLCWp1jGJhwQy2oiiKoiiKcnY0\nKeXp2pu8J1QqrTNvpFywBgezC15DKSWdlkuv65/2GFJKJkZb7H1pjMp4G4BiOcXKbVn8fJtHq48x\n5o4BkDfy3Fi6nhF7BE0TSBlxsHuQR6q/wolc8lYOXdMJopBQhoDk0sJ2tqW3ITSdrJUhadjkCkkS\n9pk/U3ICl0pvmjAKEJpA0wTFRJ6M9d6Zczo4mGVitEpYr+PPNHdKLI1H5ZyuxNafquAeO8b4//p/\niBwHa8lSvNFjAGiGgb1uPUY+j0imEKk0wrJoP/8c7huHAUht3Ub+pptnBZciYRH5PtIPqD3wM9q7\nnkUkk6Qv2U5q0xas5cv71723Zw+NRx+Js66aRunOj5K9+mrMgQGEHY8iinyfsFEn7HRmlSNHvo93\n7ChGqdSfrYsWd0g+ObO7WJzu91C58Knrt/ipa7j4qWu4uKnrd2EbHFy4Ik5leJVFy/dCWg0Hx/OQ\nSCzd7N/XabvUp7vUprvUqz3q1S7OTFA8tCzLqs0FtLzH883neWHsBSIkq5OryOgZXmnv5seT/5cN\nqfVcmb+S55vP82pnL7qmc+uKm7h86JJ5GzilzTRZPUPgSwxDnPX4HdtIMJwapNKbIqFbFBOFc87o\nXohkFOFOTeONjuIePcr0j35IUK+RvfY6Ch+6Fb3VxCiVEYnErP0i18Wv1an85/eJul2Kd95N9oqr\n8KtVui+/SOflF+ntfXXex7Q3bKRw84dmdTkWSRsjX0DY9swa30lKd30Uc2iI+o6HaO18itbOpxCZ\nDMkNG/GOHsGvVEDTSG/bTu76G7BXr4nn2Z6UmRamiRgYxCgU427QTg/p+QjTxF69ZmYjDT2Txcip\ncUCKoiiKoijnk8rwKhe8cjnN9HSn/72Ukm7bo9vxaPsd2l6csc2YWeqjLvv3TDI10Z51jGTKZGBJ\nltUXFZEZj72tvTzTeJZ22CajZ7i++EFWp1YBUPEq/Kr6GBWv0t9/KDnAx9bezkBydhmq0ASmbpK3\nctjG7IDtzYpk1O/K/F4howh/YpycLXjjZ7+g/vAvIYrQs1nCVgt73XoGPvkbcbZUi7sRM7MeN/I9\npn7wn3RffjFeM3v3x2Z90CClJGw1iTodwm6XqNsh7PVILBshsXxFvJHQEMkkRi4/J6COm2FVCVtt\nZBjgHDxI99Xd9Pa+StTrzQS6l5C7/kbMgQHMwSH0ZPLsnncY9rs1a6YZz+d9k+OPLjTqk+3FTV2/\nxU9dw8VPXcPFTV2/C9vpMrwq4FUuaF2/S5T0cFsRaTNNQrdo1Hr0HI+G28ALPF6rv077DUFwNInX\ni9deDi3NMrwsR6GcolBKghHR8jvsae7h2cZzNIIGAsH2wnauLl9BwrQQuoYQ8QggScQL1Vd4ZmIX\nW0obuX7ZtehCJ2EkyJhpTGFgCOM9F6DOR0YRUbdL2Innu5pDQ2cVvEkp8Scn8SuTNH56H+19r6Fn\nMpQ/8SmsZUuZ+j8/xHl9P0apzOBvfRazPDBr/+bOJ6k/+ADWyHKGP/+FOZlRzdDRTAuiEBnJ+N8w\nQjPNeA5uMolIJM54rsebXDEzXkpGId6xY+jZLEahCELDGhrqlzC/X6n/6Bc3df0WP3UNFz91DRc3\ndf0ubKqkWVmUpJTU3SZZ26Ljd+n4XfyuBFenGzhMuRWe3vcyqf0rMAILqXssWZth1aYyIwMDRDKi\nFzjUgzqvN/fzTP1ZakEdgWBbcSsfXH4VeTuHrSdIGsn+yJ6W16btd7lyeDtXDm8HwBAGhUSelHlh\nBD0yDIlch8j14vm0Moo7Ch/vKiz0eMyOaaKZRjyv9U00QDo+hifqxtlTGYZxeW8UIaMQa2j4jKW5\nQXWazssvMX3fj4g6nXj+7MfuwVq6BD2TZfCz/4P6Qw/SevIJxr/z/5LavPXE8whDunt2IzIZBj59\n78zIIBEHsXYSYdtvW2mwnsmgJRIE1WminoMmdBIrVsZ3qmBXURRFURRlUVMBr3LBavsdgigA4kDN\nc0J6bR83cnmmsovGKzr56jqkiOisOcrh0m7G7RzD1keo9DSQkilvisdrTzLmjqGhcXFxM9cvv4bh\n9CAZM41t2HOytEW7QD6RmwmyO6TNNBkzPe+63fNFSknU6xF1O/F81VqN3v7X6O1/jbDZwBwYwhwe\nxhoaxhweRk+lkb4PvV58AA30XB4jn18w4ymDgKjXI+x2CZ0u3tFjuIcP4bxxGPfoEaTjAJC77noK\nt96GtXTpgkG0V6kw/d8/ovXUE6DrLPvEx9EvvgyzVOx3Q04sXUbxttuxhoaZ/sl9dH69a9YxtESC\nwd+4FyObQ9g25sDAO7b+VZgm1vASwnaboFZFhpEKdhVFURRFUd4DVMCrXJAiGdFwmxxpHePRiUPU\nWi3aTg8ncggmDYYObibv21gF2HbNUqzMCI/XAna3d/PD8R9xy/CNHHWO8nJtDwDrC2u4deVNrMiO\nkDHTJ+bYLkBogqyVIWst3EH4LT0/x0H6/hlnrPbXpna7RN0erWefprf3VbyxE/OBEQJvdHTWfpod\nN2gyCjNf+QJ6oYhZLmGvWdvvWhz5fhxEd7uEjos3Nkp398t0d79C2Gz2j2cUiyQ2bsI9+gbNJx4j\naDQYuOeTWMtGEIkEUsp+dtk5fIiJf/5feGNjGKUyA5/6NINb1tMWyVkdmUUigTU0BJdsx16/gajb\njYPxmS9h2wjL6p//+aBnMohkkqBWRaQzKthVFEVRFEVZ5FTAq1yQGm6TiW6F/9j3XwTyxEzUodH1\njBzdCJpk5cVZVm4qkEmkkVJyY+l6hpOD/GrqUe4fexCAAbvEh1feyCWDW8lbuXc1Swszc2VrtXiE\nDRC2W/N2KA47HYJ6Hen7yCik/fwuGo/sIOp2QQgSq9eQXL+B1EWbMAYG8SfG8SYm8Ccn8CYnCGs1\nguo0/sT4vOchkqmZsmAdzTDRTJOg2SCs14E4u5q+ZDv2ug0kVq7EyObi8+p2qPzHv9F95SUmWk0G\n7/0MIplEhhHesaP0XttH69mnkZ5HevulFG+/E2Hb2EuG6XWjuedhJzEHB6FSQU+mZt2nmQbmwOCc\n1+adpuk65sDgeX1MRVEURVEU5Z2hAl7lguOHPtVunZ8ceIBAhty98jby/gDTewOOHe2QSOpsuX6Q\n4cECKTM5M7tWo5BPUxApRspDPDb6FBsK67hsaBsDyTJpM3XmB36byCgict1+tlITAjSNsNkkaDYg\nkvhTFYJmk8TIciLXixskFYtEjhMHup4HQO/1/dR/8QB+pYJmWeRv+TDZK67CKBTibGQ6LrW2hoaw\n17TiQPp48yUpibpdgnqNoFEnrNcJGnWCep2w2YzXADu9OKgOAjTLIrV1G6mtW0mu3xCP0LEScVCs\nG2iGgfQ9hj7/BaZ/9EN6r+5h4p+/g7l0Kc7+1+LOxsTBcvme3yB7xRWITAY9ncFIp6E7f6MHPZWG\nAYhcJw6+DePE+uN3+QMKRVEURVEUZXFTAa/yrukFPZpui6yVIWkkCfyIVtNhul3j4anHmexNsSm9\nic3ZTbz0+ATH9nVIpHWu+fAqBor5/trbhG2QySUQQpANk+iaYCSzFF3oDCbLWPrZN2s6G1LKeQOx\nyPcJm824m3E0t/l52OnQ3f0ynRd/jTc2Ft+oaVgjI9ir1pBYtZqo18OfnMCvTOJPThLUawCkL72c\nws23YA0vQS/k56ydFYkEIpHAKJYIO22k4xJ5HprQ0NNpEiPL4/E42Sx6Oh2P/yEurQ47bYJ2Ox4X\nlEyip2cC6XnW+mp6EnvlSgbv/Qy1+39G6+mn8Kcq6NksmcuuwN54Eelt2zBLZYR19q+7nk6jp9Nn\nvb2iKIqiKIqinA0V8CrvijAKeeTokzTdJpcNbkP4FsK1MITOgfYBft18gZyR47rCtezZOcWxfW1S\nWZMbb99AJhOvq9Q0yORs7KTZP66pmyxJD9H0WmTNDLrQ37ZzllLiHDqIe+gQiVWr0DNZhGWi6Ua8\nxvZ4gyhAhgH+9DR+pYJfmcQbH8M58Hq8zlXTsDdsxBocxHnjMN6xY3hHj8Ljj856PJFMktx4Efmb\nbsFasgSjWMLI5U57jpoQcfnxzNJgGUVx8yqYtzRY2DbCtjGKJWQYIkxzzjZz9jEtEstGKH3046S2\nbI1LgJctw8jm4qZY+tv3miuKoiiKoijKW6ECXuW88b0AKUE3BM9VXuDHB+4H4JnxX3NV/krW6Os4\ntr/FvtoxVgSXsVRfxiuv1Og0fHIFmxtv39gPbk1LJ5u30fW5WUihCQqJ/Js+v8j3iHpOPKbmlOym\nDAJaTz/FxL/+77hbsRBYy0awV6/BXrmK0On1g1u/UiGoTsfjgU5iDi8hfcl20lu3zWreFLku7kwn\nZJFKxZ2WB4f65cqaLjAHB8+pgZImBNpZrIHVjpden+1xDQNryRI0PR5/ZOTz71gHZUVRFEVRFEU5\nV+odqnJeBEFIvRpnQCvOFD84/GM0NLZkNrO3s49nD77C5AEL3bfIshSALhJN81m6PM+V168mYcc/\nrulsglT67S1TjlwXf3ICGUaEjXpc+pvNoek6kdOj/vAOpn70Q5CSzGVX4E2M4x07inf0CM1TjqUl\nElgjI/GooMFBzMEhzIHBfna23yjK0EHoGLpOYmQEdB0Z+EjPR3ouke+jGSbW0NAFGUxqQmAND7/b\np6EoiqIoiqIoC7rw3kUr7zlSSlr1eIarF3j8dPxndMIOVxeu5urCVSw5tonxvT3QJGMr9mANBdy+\n5FaK6Ty5ZIZiMU293sUwBbl8Et04+0zk2Qi7XfypyolmT2FEUG8QNJsIO0n94V9S//n9aJbF0Od+\nm8zlVxB1uvi1aZyDh/COHUWk05gDcXCrZ7Oz1vgKO4GeyaIlrDjQPW0jphNZXDmTIVaNmxRFURRF\nURTl3KiAV3nHdVouQRARyYjHp3ZyuHmUEWMll+lX8fIjFaYmHOyUwZIrdHQrw7bsNsqpQn8GrqZB\nOmORTFvnHPzJKEKGwZyAM2y18KvTyCjCef11vInxE12CdR1vbIz2c88gMhmGf+d/krn0MjQh0FNp\njHKZxMgKwm4H6QcQBshwZvTOTLMoPZt7U82bTqYCXUVRFEVRFEV5a1TAq7yjPDeg1/WZHGvy5CP7\n8Z0UW/gIAI9zEIBlKwtsv3aEntZhrbGUrJ0mb+fRADQYGMpQb/QWfpDTiFyXsN3CHRslmK5iDQ/H\na3RNE4QgbDTpvPwizaeeIJiamvcYRrnM8Be+SOqiTbPWuWpCzOkuLKWEMDwxjkhRFEVRFEVRlHeN\nCniVd0wURbQaDp4bsPPRA3hORCdbZSBVYjBdwjR1SoMZVq0roWkaQ9k8WAGpU2bmmtbpf0yllMgg\ngChChiGEITIMCbsdItejvetZ6g8+EG8DGMUi1tJl6Lk83ZdfJGy3QQhS2y4htXkrzBxPhvH26W3b\nsVetOqsAVtM0uADX2yqKoiiKoijK+9F5fWe+c+dO/uiP/ogNGzYAsHHjRn7v936PP/uzPyMMQwYH\nB/nmN7+JZVncd999fPe730UIwb333stv/uZv4vs+f/EXf8Ho6Ci6rvM3f/M3rFix4nw+BWUBXuhj\nCmNWGW676RKGEc88dQC3G1JZtp/SJp07ln8Y46RxQUJo5ApJTEsHTl/+G/k+0nGIfA/pB3GTJz+Y\nd9uw3Wb6J/+Ns/81hG2TufwK/EoFb3yM7u5XANAsi+y1HyB71bUY+bmdnUU6hVkeUNlaRVEURVEU\nRVmEznsq6uqrr+Yf/uEf+t9/9atf5XOf+xx33nknf/d3f8cPfvAD7rnnHr797W/zgx/8ANM0+fSn\nP81tt93Gjh07yOVyfOtb3+Kxxx7jW9/6Fn//939/vp+CcoogCpjoTqKhYcsUprQI3IggDNl/YJSx\ngy16qQbhqip3LvntWcGuaenkCjZigYBSBgFBu40/PUXU6xH5PmG9TtBoEDabBM0GYauJjCL0TBYj\nm0PPZYl6DrUH7yfqdrHXrKX8yd/AWroM6TqErktYreFXp0mMLEfY9qzH1HQdkcmgZzJnNZdWURRF\nURRFUZQL07tee7lz507++q//GoBbbrmF73znO6xZs4Zt27aRzWYBuPzyy9m1axdPPvkk99xzDwDX\nXXcdX/va196181ZiUkrGG1N0Oh6+F0HUBU2QMmw6jssrT08QaRET63bz6WUfI2/lMC0d09Tjfy19\nVlY4ct04sPU8pOsSdjtM1iaY3nsgHgM0eozIcc7u5HSd4kfuIHvtB7CWLusHr1JK5PASItedMytX\nM01EKqUaRimKoiiKoijKe8B5D3j379/Pl770JRqNBl/+8pfp9XpYM11sy+UylUqFqakpSqVSf59S\nqTTndiEEmqbheV5//4UMDmbfuSf0PuX7Ib2OR6VeJ/ADbMvEnnUZIvY9MUnowuSKvdyx6Sau37Kd\nbDa5YDAZtNs4zSaSiO74EaaffIr6r19A+n5/G6tctRFhtAAAIABJREFUJrXpIqyBAaxCHrOQxywU\n0ITAbzTxm038RoOw51C68nKSK1aQXLZUZWovAOr3cPFT13BxU9dv8VPXcPFT13BxU9dvcTqvAe/q\n1av58pe/zJ133smRI0f4nd/5HcIw7N8vT8m2nevtp6pUWm/+ZJUFuY5Ps+4QRCFTvSqHuwfZMf0I\nA1aZleYqlsgRvIpO5XCPTqbKqotKbMpehOuGuG573mOGrRbe1CTtXc/T3vUM/sQEAHqhQPnKy5ED\nS7CWjaCn0iA0NF1H03Wk0PF1HRmFRHYObVD2VwE7pkFkZenWHeAss8LKO2JwMKt+Dxc5dQ0XN3X9\nFj91DRc/dQ0XN3X9Lmyn+zDivAa8w8PD3HXXXQCsXLmSgYEBXnrpJRzHwbZtJiYmGBoaYmhoiKmT\nRsRMTk5y6aWXMjQ0RKVSYdOmTfi+j5TyjNld5e0VRRHtpgtA02tSaU3x3Cuvs6x6OZabpBkkaNKJ\ntxUB0eYprivfzlC5uOAxg0YDvzrN9H3/TfelF0HTSG7aTOayK0ht2sTw2hGm6w6aoaPpxoINpKSU\n8Rrdbhfp+ZgDA2iqY7KiKIqiKIqivG+d12jgvvvuo1Kp8MUvfpFKpcL09DSf+tSneOCBB/jEJz7B\nz3/+c2644Qa2b9/OX/3VX9FsNtF1nV27dvG1r32NdrvN/fffzw033MCOHTu45pprzufpK5zovHzk\n6BT7X51gerTHkNwImiSR1gkTHl2zSd2YIip3+NiKjzAyMIiuzx+k+rUaQb1O/cGf033pRayREQY/\n/Rn0fA6jUMTI5TBzWXT3zGtqNU1Ds5MIO/l2P21FURRFURRFURah8xrwfuhDH+JP/uRPeOihh/B9\nn69//ets3ryZP//zP+f73/8+y5Yt45577sE0Tf74j/+YL37xi2iaxh/+4R+SzWa56667eOKJJ/js\nZz+LZVl84xvfOJ+n/77nOgG9rs+jP99HZTwuTXaTHZIrQq7etAXTOhHUBjJAQ6OcLZDNzp6rK6OI\nsN2Ouyv7Ac3HH6X19FOYA4MM/tb/wCyXMctllZ1VFEVRFEVRFOUt0eTZLoRdxFS9/VsXRZLaVIeX\ndx1j96/HECWP14afpVROcffwXQhtbgZX1w02rFiOORO4Rr5P2GoSttsQxT92reeepfazn6Dn8wz/\n7hexV6/GyOVmHUetmVj81DVc/NQ1XNzU9Vv81DVc/NQ1XNzU9buwXTBreP9/9t40uK7zvPP8ne3u\nG+7FxQ4SAAmKIkWKpCSK1GItli07irc4tuN0MpPp9FSqu6pravKpK1Vd1dU1VV3TVa7pnk6mMk46\naWeSdNtW4thKvEiyJGulNkoUSXEnAWIHLu6+nX0+nHsPAQIgQYqiCOr9ValAEZf3nnPec8/7Pu/z\nPP+/YONSLTfJzVc5cWQGJeRydOglIsEQT2SfQJZVOsJJwMW0LSzbwnItutMZNFXFdRysYhG7UgbX\ny/CaizmaZ89Q/OVzyJEIXb/9uwQH+lcEuwKBQCAQCAQCgUBwvYiAV3BVDN2iVjV4+5UxXBcuDr+H\npLk8mf08iWiMgUw3sfClvlnXdXEcF0WRsatVrEIBM5+n+u5b6JOTGLMzuIYBgBQI0PXt3yE4MIDa\nkV7rEAQCgUAgEAgEAoHgmhEBr+CKtC2Ijr07SaXUxB0oU4jP8lDnQbb0DdITzxJUlitlS5KE5JgY\nC4vY1SrlQ29Qfu0V309Xy2YJ9PYT6OsjvHUULZtFy3at6c8rEAgEAoFAIBAIBNeDCHgFa1KvGdQq\nOnPTZc6eWCAQk3i/5w06AikObrqXntjKYNd1XexSCbNYoHH6FMVnf4FVLCBHo3Q8+UUid+5EDgYv\n/QNZQuvqWtNqSCAQCAQCgUAgEAiuFxHwClbgui7Vsk6zYaI3Ld55dQxJgrmtH+LINp8fepTeWPeK\nYNfRdczFHMbsHIVf/JTmubMgy8TvP0jy4UdQohGQFfATuRJqKoWsCS9lgUAgEAgEAoFAcOMRAa9g\nGY7jUC42MQ2bSqnJa788S6NuEh01ORq4wLbUFu7O3rUs2G2LUlmFPOXXX/PKly2L0PAIHU9+kUB3\nN0oqhRKLi7JlgUAgEAgEAoFAcNMQAa/Ap9kwqZZ1XNdlbrrMoZfOYxo2/XfEeaXjGVRX5fObHyMR\n8GS/XdfFrlSwS0UaZ8+S//k/YS0uIsdipD/3BSJ33YWaSKImk6JkWSAQCAQCgUAgENx0RMArwLYd\nquUmhm4DcO7kAu+/eREkiTvuy3A2cYR6pc5DfQcYSm5CkiTsWg2rWKA5NkbplV/RPHsGJInYfftJ\nPfI4aiaN1pFGUsUtJhAIBAKBQCAQCD4ZRDTyKadRN6iWdQBsy+HI25OcP7WAFlTY/kCaZqzE0blj\npIIJHh18kBAqxtwsjdOnvUD3/DkAgps2k3ri84Q2D6Gm0yhLbIoEAoFAIBAIBAKB4JNABLyfYtol\nzACFxTpvvXyBSqlJNKlx5wMZZqQJfjn/Ig4On9v0GBkrSH3iNPmf/JjG6VMABDcPkfzMo4SGh1ET\nSZRkUvTpCgQCgUAgEAgEglsCEfB+StGbpuer67icPDbLh+9N47owMJpkYEeEo42jvFl8C01W+drQ\nF7lb6qVx7Bi5v/sBdrlMcNNmko8+Tnh4GCUe9wSpRPmyQCAQCAQCgUAguIUQEcqnEEO3KBebNOoG\nh146z+J8jVBE464DPWgdFq8UXuVk9RQxLcrXh3+N3pqK+c5bFJ97FhyH5COPkXzss2ipFHI0KjK6\nAoFAIBAIBAKB4JZEBLyfMkzTplxs0GyYvPyLM1RKTQaGOti1v4+iU+DnC79gojFJT6SLr438GomF\nGu4vnqP44YfIkQidX/06sXvvQ00kPulTEQgEAoFAIBAIBIIrIgLeTxGmaVPKN9CbFq886wW7ozu7\nueueXvLNIodKh5hoTDKS2MyXh79AcLGE+5Ofo587R3BgkMxvfpPwyBaUSOSTPhWBQCAQCAQCgUAg\nuCoi4P2U0Gx4PbumafPqc2cpFRqM3NHJ7nv7KekVTjZOcLR0jM5Qmi+NfIFQsY7085eonztHaMtW\nur79OwR6e5GDwU/6VAQCgUAgEAgEAoFgXYiA9zbHdV2qZZ1mw8SyHF57/iz5XI3NW9LsPbAJ3daZ\ndiZ4af5XhJQgv7H114lUDZSXDlH54AMCfX1kv/3PCA4MCFEqgUAgEAgEAoFAsKEQEcxtjG07lIsN\nLNPB0C0OvXSe3FyV/s0p7nlwCNu1ycs5fjr1CxzX5UvDT5KpSwTeOkLx9ddQ02m6fud/JjS4CUlR\nPunTEQgEAoFAIBAIBIJrQgS8tymu61LKN7Bth8JinUMvnqNWNegdTHL/Z4aRZKgrZZ45/1OqZo1H\n+w6yxYgROn2O/LM/R45GvWB3eEQEuwKBQCAQCAQCgWBDIgLe25Ra1cC2HcbOLnL4jXEc2+XOu3vZ\ncXcvkixhBKv8bPw5Zmpz7Ehu5V5nAOW1w+RfeRkpEKDrt3+XyB3bkTXtkz4VgUAgEAgEAoFAILgu\nRMB7G2KaNrVyk/ffmuT8qQU0TeHAo0P0DaaQJAk3YvDLiZc5kT9NXyjL552tSD/6KdULF1ASSTq/\n/g2iu3YJgSqBQCAQCAQCgUCwoREB722G67qUiw3eff0i4+cWSXaEOfjYFmKJILIsocZdXph4nTdn\n36VDifG1yhDSz3+EUakQ2jpK5stfIzgwgBKJftKnIhAIBAKBQCAQCAQfCRHw3mbUawanjs4yfm6R\njs4Ijzy5DVVTCAQVAhGZ1869yPOzL9Nhqnz7wyDqBz/DcV2Sj32WxEMPEchkUWKxT/o0BAKBQCAQ\nCAQCgeAjIwLe2wjLtBk7k+ODtycJhlQOPrYFVVOIxgKEwirvnHqZn06+xP5TDe4/0UQ2plGSSTJf\n+iqRO+5A7cyKnl2BQCAQCAQCgUBw2yAC3tsE13WZmSxx6KXzIEleGXM8SCIVQlVlJsePc/jNZ/id\nw0ViDQcpFCL5uceJ33MfaiaDmvL6ewUCwfpwXVd8Zz4GmoaFpsoosvxJH4pAIBAIBILbABHw3gZY\npk0hV+OVZ89g6Db7Dm6iuy9BMh1GlqA2Mc6ZH/43Hj+dx1FkwgcPkHnwUZR4HC2TRg6FP+lTEAg2\nFPWmSb6igwuRkEokpBIKiMfpR6XaMMmVGkhIREIq8YgmrqtAIBAIBIKPhFhJbHDqVZ1ivsFbr1yg\nVGgwckcnozu6/WC3fuokY3/1XQYWihTSQYa+8bskuwdRE0mUZFJkqAS3NY7r0tAtZElCVSQUWUaW\nJVzXxbIdTMvFtB0cxyUSVAkGruw5rRs2+UoT3bT9vyvXDcp1A0WWCQcULzupyGiKjKp63y/bdrFs\nF9txkGWJaOijtQ44rotu2ISDt+Yj3HFcqg0Ty3bQVBlNlQmoCrK89vOmHewCuLjUmia1pommyETD\nGuHA1cdHIBAIBAKB4HJuzdWS4KrYlkO51GD6Yol3Xh2jXjPI9sTY98BmUpkw2BblQ28w9/3/TqDZ\n5MRImNGnvkVHz1bUdEb06gpue6oNk2JFx3KcZX8vSxKu6wVVSynVdC9oDSpEghqyDLbjev/ZLoZl\n09CtNT/PdhyqTWfN3y+lEbJIJ0PI17jhZNkO5ZpBtWHiuC7hoEomEUJVrr/813FdbNvBsr1NAMt2\n/XJtSQIJkCQJNdSkVNX9qyZLkh/Mtj/fMG3KdYNaw1pxfQE0RSYWCRAPa8uC32rDZLHUZLHc5NCx\nOYIBha6OMJ3JENlUCNN2KKIjSxKhoEo4oBDQvM2F9VxDy3ZoGjayBJGPuNkgELSxHQertWlmWg5T\nC1UmF6o8ce/gR/pOCgSC9eG6LnXdIhJU10zgeHOSSVdHWLTK3EDqTYu6brY292X/57WuaxzXxbIc\nAtrHu6EtAt4NiGlY5BdqfPDuFGc/nEeS4M67e7lrXx8dnVGshXkWn/kxlUNv4MgSL+6P03XPA4xu\nvRctKhSYBbc3q2Vhl+K4KwOxNrbjUG04VBvmmq+xLIdTE0WOnc/jAv2dEfo6o/R1RgkFFHKlJuOz\nFcZnK1ycr2JaDtGQSiSkEQ2pJKMB7ruzC8Ny6OoIr2th3DQsynWTRnN5INnQLaZzNTriQeKRwFXf\nx7RsdNPBMG1vkW46KzYE1sJVFApV3ftz6xq2FxiyJKHIEqbttD7HYbHUxHIcEpEAsVaAa9oOhYoX\nOMfCGoloAN20WSw1OTGe58evjq06bplEiM09MTZ1x9ncEycZvXSumiKjaQqqLCHLEnI7UJckDNOm\nYdiY1qX31FSDZDRANLT2Ask/Z9fLzGuqWCTdCiwUG4QCyrru9RuJ7Tg0dJumYfkbQ7bt4uJVbZy6\nWOTtkwuMz1YAmMzV+BdP7bipxygQfNqwbIeFYgPdtNFUhXQ8uKzqybIdFstNf6N6Lt+gO/3JB722\n4zBfaNARD27Ilh3XdSlUdMp1Y8XvJCQCmkwkqBIOqlcNYmtNk3xZ96rfJIlQQCEYuLSpfaVjaOg2\nAU1e9+bixrvSn3L0psXF84u8+asLVEpN4okg9z08THd/glhYpvLqyyw+8w/YxSJmIsLfHQiiDvbz\ne3d/DU0NfdKHLxBcF+0FpqbKa5bF6oZNqW5Qb64drC7FcVzmiw2mFmrUmia7RjJ0xIOrvtZ1XaZy\nNY6cXeT4hTxN41IAdXqi6P85qCnLArZYWCMe0ag1LfIVnXas/c6pBQ7s7OYzd/cxkI2tWprsBd8W\n1brhB5KlmsHEnJdFypebDPcm2DmcxnFdak2LaEhFkSWkVgAqSRK6adNomhwbK/Dy+1MUqgYhTSEY\nUAi1JhXHaWd3HT/AS0YDpGIBkrEgkZCKPl5kbKrEXLHOfKGBbbvEI17QmogEiIRUChWdXKlJoaIv\nOxdJgmhIIxkNsHUgya4R75grdRPHcfnl4UlePzaLqsh8+cEhUvEguWKDhWKD+WKTmcUah0/nOHw6\nB0AiGiAc8Eqk28FtMhpg10iGrQOJKy5oTMsmV2pQrMokIgFUpX0/eT8Ny6ZQbpKv6BSrBrppcefm\nNINdsatOrI7j0jQs6rqF47iEAqp/ja+GaTk0DAtFlgioigiyL6NcM6g1TepNC0WWiYQ+vuWL67re\n90b3qjoMy0Y3bGbz9VZWw6LeNKk2TE5eLFKpe8+cod449YbF60dnGe1P8sie/o/tGAWC2w3bcXBd\n1hXANHSLhWLD38A2LZu5Qp1IUKUjHsKwvI3UpRvchmUzl2/Qk45csb3m4yZX8jbk5/Je0JuIfvQN\nvPamXK1pEg1pxMIfTyWTadnMF5vLNpGX4uI9O3XTplDV/VavUNCbC9tja1o2i2WdpnGpas5pZevr\nukUB/HamaEjz50PTcqjUL1W5gbfu8l535TlBct0rpDtuExYWKp/0IdwQmg2Tkx/M8NYrY9iWw9Y7\nu7jrnn5S6TByYZ6Fp39A/egHIElY+3byvW1FmrLNv977v7I1NfJJH/51k83Gb5sxvN2pNy2KVZ1k\nLLCsT/VKY2haNqWqF9RJkuSFHRLgcqnEtpXVlPB2AKNhjUhQRZYl6k2TUs1YkRmsNUwOn15gbLaC\nLLfKbxVvNzBf0ZnO1TCtS9lNSYK7htM8uKuXrg5PyK1cMzh6fpEjZxfJlZoAxCMau7dkuHtLhmBA\nYTpXZypXYzpXo1jV6ctE2dwTZ6gnTiYRQpIkXFwcx6VhWFyYLvP8u1OUawaxsMZn7+lnz2gWVZGR\nJfyJOF9qMp2vMbtYZ2axzuRCjXJt5Y4qwFBPnF0jaUb6k8vKhV3X5cJMhZfen2JyvgZ4waJh2suC\n9qVIElxpVpAk6EyG0FSZSt30F/ttIiGVbDJEZyq85DUG5ZpBuRXgAvR1RrlrOM2piSLjsxXSiSDf\neGwL3R2RFZ/pOC6z+TrjcxUuzlaZao2d67o4rovjXMrcR4Iqd42k2TmcxrId8mWdxXLTX/z0pCP0\nZiL0ZqKkYgGKVcPPxl+crXhiZJcRDak8dXCIAzu7SUYDfma43UvtBUcWlbrJhdky56fKlOuGv2nQ\nEQ/R3REmmwoTC2uoqowie6X1Dd2ioVv+poZ/nfFKvQOqQn82SjCgfKTMxEZ+jrYrGV4+Mk1nMsSu\nkQw9mcgNzY60y97rukVTt6g1Ta9SY67KxbkKs/n6qt+LoKZw99YM9+/o5s7NHRSrBv/+v72NZTv8\n0e/ew1BP4oYd40Yew1uB9vPiZmT4LNtBkljxWbfyGFq2Q6GiEw6q/vx6syjXDYoVHcd1UWWZgCYT\n1BQ0VUFpVe+0f5aq3mbkam0z4K0T1vodQEBVrjvo/ajjV6rqfqVUm2hII9NqcXJdl6bhzSe6aRMK\neKKYwcs2TV3XxbAc9NYzSzfsZefcmQxfd9DrtjbQDdNe1trkuN6a6ErX9mqoikxAlWno9jW9T1sD\nZGmAfDkSEvfu6lv79yLg3RjUqjpvvzLGiSMzKKrM/oeH2Ly1k3g8QPXQq+T+7oc4tRpSd5ajnxnm\nV4FJLNfic5se5Stbvrihxalu5QnidsJx3I80wbX7MNsPMU1V6IgFiIS0VcfQtGyKVYN6c/V+z8u5\n3AZIwpsAl5bkuq7L1EKNt0/O8+FYAdtZ+32zqRAD2Rj92SiKLPHGsTnmi55o0rbBFLbjcH66jOuC\nIkts39zB3VszjPQm1nWdwgGVdCKEokjohu1PYoblldi+fmyO147OYtkOiiyhKjKq4v1sZz+XEgmq\nDHbFGOyOMdgVIxULcOpikaPn80zMVy9dFwkSkQDJWADLdpjO1f1zeurAJob7ktSaXnl0eydWUSR/\nM0CWvVLgUs2gVDUoVnVqTYv+7gSxgExnannPcDsTXW+aJKOBK/bI6qbtHfO5Rc7PlP0AYvumFF9+\naIhQQEWRvesgSxKSLCFL3kRrmDaWvXr5tSJLTC3UOHI2x9ELeerNtSfFy//d0nskqCn0ZCLEWrvF\n0ZCKYTkcOj6H7bjsHOrg1x8YojMZ9oPcqVyVsdkK56fLTM7XrlgyDxBQZVKxIMlYgHhEI6ApBFSZ\ngObtfhfKTWbydebyDX8T545NKR7d28dANk5IUzwBNkX2F4CyJLUCf5daw2C+2GQgGyURvVSxsFGf\no5btMDZb5vu/PMu56TIAu7dkeOrgZjZ3x5dlz9tZDsdxUVr3kKq0F8srgxzTcvxsbftaN3WL14/P\n8uaH8/6GmCJL9GejDGRjxCOap8zeKtnrTIZIJ7z/2r1r75ya4//50XHSiSD//p/vv2F949czhreS\nfVpDtyhUdKIhlXgkcFMDKttxWCg2MUybdCK0ZjDgui6uy1WPzXHcZfOW23pGNY1LbRSaItOdjix7\nXi4dw2rDpGlYdCY/eacMx3WZy9f974GE5OlZhDT/+WLbjv+8VGTvGdSes663Z103bfLltVuQLudq\nwex6CajKuluKlvJRnqNNw2Iu31hd30JV0BSJpmGvOoeoraqWdtXW5QHualxr0GtaNpW6uSx7utG4\nb9faVTUi4L3FsSybUqHBa8+fZWq8SCQW4IHHt9C/uYMgJnN//T2qb78FisLC/dv4yUidqtMgpkV5\nuP8gn9v0CEF19TLNjcJGXahtFFzXZbHcRDdsejPR61qElKpeFu21Y7OcnSyxe0uG3VvSaKpCQFXo\n7opTyNf83ULLcanWDRbLTaZzNWbzdWRJ8spjowESEQ1FkZldrDO96GVP5/IN0okg+7Zl2b0ls6wM\nuFo3OXYhzwfnFpnNewFeZzLEvdu72L0ljSLLmJbji8vEwitthFzX5fRkiVc/mGFqwcuG9mej7Nna\nyY6hjnUrIsuSdMUFlV+q3DDJFev86v1pFopNP5vdDuqyqRA9maiXjUxHSMYCay5cixWdYxfyzBUa\nlGo65apBpWHiujA6kOSxvf3s3pJZtvB2nEvlQ5bllTNfaZLrSEUpFuuEggrRkIaqSH5m1XFdXMfr\nd7Uc7zzsq7xftWFyYrxAKKBw13AaVVFIxbx+37XO03G8cinTci4F6S2RDMv2+oarTYNzU2XOTJQI\nhxQyiRCZVkAiSTCzWGc272XNc8UG6VZ/8HBPktHBpD9u7YWv7bicvFjk718+x9RCjUhIZfeWDDO5\nGpMLtWUBc19nlC19CUb6E3QmQpc2DWq6v3lQqhoUqjqGeeXe6UwiRE86TLFm+PfjzqEOHtnTRyLq\nZaYLVZ1iRSdf1lkoNcgVm37/eUCVuXtrJw/t7mVTV4yB/hSVlgr2ajR0C8t2rnj9byT1pkUoqFxR\n4MRxXcZmynzv56eYmK+ypS9Bw7CZztXoTIb45mNb2Tmc9jcfrrhodgGp3X8u+X/ZPlfTsnnrxDyv\nHZ2ladjEwhr7tnUy1BOnPxvzS+okWpsMrYxTu5z/cv7HL8/w7NsT7Bjq4H/7xm5M0xO+M1r3L5Lk\nV3RI7T9LlzYwNFVe8cxZOhc6jku+0vSC+lb1SkCTcRz8BXHTvNS/7vW2e58Tb7Uh3Cxsx6FQ1qku\naTfxn/dXCXwd11N815SV12O9eOWujWUbZpcL/lm2p93QbrPwNAY0NPXShorjutRarzHWKOm8HFWR\n6VkS9LbHsFwzyFe8qqFkNLhmO816cVz3it8ly/a0G9bafJkvNtbdDrQaa5Wurka7ZaDWmgdvRAB7\nPciSRCYZWtU1oZ3l1BR5mTvA9a5HbcfbfLbXqZlxo1hP0KsbXvnxlbKnGwUR8G7AYMm2HKqVJudO\nLnD8vWmqZZ3O7hgPPrGFbE8C88JZZv/rd7EWF5Gznfz8wRQnImVUWWV/9z4O9N5LV6STeGBjiFS5\nLfuYhm6TiAaW9a+JgPfjw3FcFopekFRvWnSnI3SnI9ekspdvBa0/euU8F2YujVMooLB3tJN7t3f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48zV2iQigX4ysPD7Lmjh0LR80pdKDSoNkxG+1P0d8VWNOM3dIv5YoOZxRod8SBBTSEcUFtl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\n7z6Ir/h0yiZehWGzZzPxXqPjwJ/pewlV9mzJl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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "id": "8T0yfWPw-7QZ", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "## Example 2: Train an agent built from scratch.\n", - "The purpose of this example is to demonstrate how one can create an agent from scratch. The agent\n", - "created here is meant to demonstrate the bare minimum functionality that is expected from agents. It is\n", - "selecting actions in a very suboptimal way, so it will clearly do poorly." - ] - }, - { - "metadata": { - "id": "1kgV__YU-_ET", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Create a completely new agent from scratch.\n", - "\n", - "LOG_PATH = os.path.join(BASE_PATH, 'sticky_agent', GAME)\n", - "\n", - "class StickyAgent(object):\n", - " \"\"\"This agent randomly selects an action and sticks to it. It will change\n", - " actions with probability switch_prob.\"\"\"\n", - " def __init__(self, sess, num_actions, switch_prob=0.1):\n", - " self._sess = sess\n", - " self._num_actions = num_actions\n", - " self._switch_prob = switch_prob\n", - " self._last_action = np.random.randint(num_actions)\n", - " self.eval_mode = False\n", - " \n", - " def _choose_action(self):\n", - " if np.random.random() <= self._switch_prob:\n", - " self._last_action = np.random.randint(self._num_actions)\n", - " return self._last_action\n", - " \n", - " def bundle_and_checkpoint(self, unused_checkpoint_dir, unused_iteration):\n", - " pass\n", - " \n", - " def unbundle(self, unused_checkpoint_dir, unused_checkpoint_version,\n", - " unused_data):\n", - " pass\n", - " \n", - " def begin_episode(self, unused_observation):\n", - " return self._choose_action()\n", - " \n", - " def end_episode(self, unused_reward):\n", - " pass\n", - " \n", - " def step(self, reward, observation):\n", - " return self._choose_action()\n", - " \n", - "def create_sticky_agent(sess, environment, summary_writer=None):\n", - " \"\"\"The Runner class will expect a function of this type to create an agent.\"\"\"\n", - " return StickyAgent(sess, num_actions=environment.action_space.n,\n", - " switch_prob=0.2)\n", - "\n", - "sticky_config = \"\"\"\n", - "import dopamine.discrete_domains.atari_lib\n", - "import dopamine.discrete_domains.run_experiment\n", - "atari_lib.create_atari_environment.game_name = '{}'\n", - "atari_lib.create_atari_environment.sticky_actions = True\n", - "run_experiment.Runner.num_iterations = 200\n", - "run_experiment.Runner.training_steps = 10\n", - "run_experiment.Runner.max_steps_per_episode = 100\n", - "\"\"\".format(GAME)\n", - "gin.parse_config(sticky_config, skip_unknown=False)\n", - "\n", - "# Create the runner class with this agent. We use very small numbers of steps\n", - "# to terminate quickly, as this is mostly meant for demonstrating how one can\n", - "# use the framework.\n", - "sticky_runner = run_experiment.TrainRunner(LOG_PATH, create_sticky_agent)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "gQt3t_IS_Gku", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Train StickyAgent.\n", - "print('Will train sticky agent, please be patient, may be a while...')\n", - "sticky_runner.run_experiment()\n", - "print('Done training!')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "oom0wB0A_Qb8", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Load the training logs.\n", - "sticky_data = colab_utils.read_experiment(log_path=LOG_PATH, verbose=True)\n", - "sticky_data['agent'] = 'StickyAgent'\n", - "sticky_data['run_number'] = 1\n", - "experimental_data[GAME] = experimental_data[GAME].merge(sticky_data,\n", - " how='outer')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "DqsagPbb_Xjm", - "colab_type": "code", - "outputId": "1d263334-e476-4f76-88df-28d0b6a271ae", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 512 - } - }, - "cell_type": "code", - "source": [ - "# @title Plot training results.\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,8))\n", - "sns.tsplot(data=experimental_data[GAME], time='iteration', unit='run_number',\n", - " condition='agent', value='train_episode_returns', ax=ax)\n", - "plt.title(GAME)\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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Bfy0GVcVVV48RDGIE7bJnMx7H0nWsRG6Tqp5meTP3/nbFiETS5dZWIpG+vpXRsMpVX9/l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x5z0oxQhMwsjoZZl+psLZBnqpLncvEqKS1JUFXIVy1GM6OMzZ3n5fNv4i0lAevdVAoenlKitT1J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ntmKZNvfuznH76nRpg0ZRpYoKci6rc/WTcWRZ5PDTlZtqnydqiZuVhCJeLMtGFAQEUUAUBVKJ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WweTHLbpixHz5U4MVlmqCfG73xuK5FQgHQ6SqFY5+1j87x4YAbbcXn4jn4e2T2ALMn0ZCIE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\njMVi/OM//uPFTLY4ics24H3llVfo6+vjkUceobe3l4ceeohHHnnkY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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - } - ] +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "agents.ipynb", + "version": "0.3.2", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "cells": [ + { + "metadata": { + "id": "VYNA79KmgvbY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Copyright 2018 The Dopamine Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", + "\n", + "https://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + ] + }, + { + "metadata": { + "id": "emUEZEvldNyX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Dopamine: How to create and train a custom agent\n", + "\n", + "This colab demonstrates how to create a variant of a provided agent (Example 1) and how to create a new agent from\n", + "scratch (Example 2).\n", + "\n", + "Run all the cells below in order." + ] + }, + { + "metadata": { + "id": "Ckq6WG-seC7F", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Install necessary packages.\n", + "!pip install --upgrade --no-cache-dir dopamine-rl\n", + "!pip install cmake\n", + "!pip install atari_py\n", + "!pip install gin-config" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WzwZoRKxdFov", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Necessary imports and globals.\n", + "\n", + "import numpy as np\n", + "import os\n", + "from dopamine.agents.dqn import dqn_agent\n", + "from dopamine.discrete_domains import run_experiment\n", + "from dopamine.colab import utils as colab_utils\n", + "from absl import flags\n", + "import gin.tf\n", + "\n", + "BASE_PATH = '/tmp/colab_dope_run' # @param\n", + "GAME = 'Asterix' # @param" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "EFY3tTITHugq", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Load baseline data\n", + "!gsutil -q -m cp -R gs://download-dopamine-rl/preprocessed-benchmarks/* /content/\n", + "experimental_data = colab_utils.load_baselines('/content')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bidurBV0djGi", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Example 1: Train a modified version of DQN\n", + "Asterix is one of the standard agents provided with Dopamine.\n", + "The purpose of this example is to demonstrate how one can modify an existing agent. The modification\n", + "we are doing here (choosing actions randomly) is for illustrative purposes: it will clearly perform very\n", + "poorly." + ] + }, + { + "metadata": { + "id": "PUBRSmX6dfa3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Create an agent based on DQN, but choosing actions randomly.\n", + "\n", + "LOG_PATH = os.path.join(BASE_PATH, 'random_dqn', GAME)\n", + "\n", + "class MyRandomDQNAgent(dqn_agent.DQNAgent):\n", + " def __init__(self, sess, num_actions):\n", + " \"\"\"This maintains all the DQN default argument values.\"\"\"\n", + " super(MyRandomDQNAgent, self).__init__(sess, num_actions)\n", + " \n", + " def step(self, reward, observation):\n", + " \"\"\"Calls the step function of the parent class, but returns a random action.\n", + " \"\"\"\n", + " _ = super(MyRandomDQNAgent, self).step(reward, observation)\n", + " return np.random.randint(self.num_actions)\n", + "\n", + "def create_random_dqn_agent(sess, environment, summary_writer=None):\n", + " \"\"\"The Runner class will expect a function of this type to create an agent.\"\"\"\n", + " return MyRandomDQNAgent(sess, num_actions=environment.action_space.n)\n", + "\n", + "random_dqn_config = \"\"\"\n", + "import dopamine.discrete_domains.atari_lib\n", + "import dopamine.discrete_domains.run_experiment\n", + "atari_lib.create_atari_environment.game_name = '{}'\n", + "atari_lib.create_atari_environment.sticky_actions = True\n", + "run_experiment.Runner.num_iterations = 200\n", + "run_experiment.Runner.training_steps = 10\n", + "run_experiment.Runner.max_steps_per_episode = 100\n", + "\"\"\".format(GAME)\n", + "gin.parse_config(random_dqn_config, skip_unknown=False)\n", + "\n", + "# Create the runner class with this agent. We use very small numbers of steps\n", + "# to terminate quickly, as this is mostly meant for demonstrating how one can\n", + "# use the framework.\n", + "random_dqn_runner = run_experiment.TrainRunner(LOG_PATH, create_random_dqn_agent)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WuWFGwGHfkFp", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Train MyRandomDQNAgent.\n", + "print('Will train agent, please be patient, may be a while...')\n", + "random_dqn_runner.run_experiment()\n", + "print('Done training!')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "IknanILXX4Zz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Load the training logs.\n", + "random_dqn_data = colab_utils.read_experiment(LOG_PATH, verbose=True)\n", + "random_dqn_data['agent'] = 'MyRandomDQN'\n", + "random_dqn_data['run_number'] = 1\n", + "experimental_data[GAME] = experimental_data[GAME].merge(random_dqn_data,\n", + " how='outer')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mSOVFUKN-kea", + "colab_type": "code", + "outputId": "c7053a43-9f59-4817-ee0a-b3c074b509b1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 512 + } + }, + "cell_type": "code", + "source": [ + "# @title Plot training results.\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(figsize=(16,8))\n", + "sns.tsplot(data=experimental_data[GAME], time='iteration', unit='run_number',\n", + " condition='agent', value='train_episode_returns', ax=ax)\n", + "plt.title(GAME)\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+jl5SStk3vomi9rLPVVXwjqh2P2s4jBUKYUcj4LgZZK2gIOM+3mTh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m5wv09QbYu9OdM3VyWYwL74Omoxx7svLBBJBjMZxCgcnpKUQRmmOLVQ1RVZGD9XNbl7L/WBdz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48zV2iQigX4ysPD7Lmjh0LR80pdKDSoNkxG+1P0d8VWNOM3dIv5YoOZxRod8SBBTSEcUFtl\ni5K3yGhaK0p82khIhFsKnDdClt+0HOYKdap1g1ePzvLWiTks26W7I8yO4TSzi3Um5qu+0Ax4u5B9\nnVEvCM5G/TKqeDSAIsFMvs7ZqRLnpsqMz1awHZeuVJjRwSQ7htJs39TBC4cnee6dCQzToScd4eBd\n3ZRrBrlSk1yxSaGqM9QT57F9/XSlIgQ1GceFuUKdv3n2NLlSk91bMty7PUuxckkcZ2y24nusZlMh\n9o5m2TvaSUerFO/yxWW7R7WnO0Gzrq+rR7NdjmbZ7ppjUGuaFCs6pu2gKnJLVVe7YcGp41wqhRV4\niIB3Y3P5+K214HJdr+z4ubcneOn9ad9Gqo2myty9NcOje/rZNpjyv/NNw+L8dJk3P5zj1ESR+cKl\nstqlVlfdHWHu3d7FrpE0AU1BwuuhTi0RZWv3n0sSN9R6aC1c18W0nHX5Jd/oz20aXvDb0FfOS7bj\nBZ6nLhY5PVGkMxXhW49tYaQvserYNXQL1/XKdVd7buqmTVP3hIJqTZMXDk/x1on5NY9PkvC9vrf0\nJZhcqHJyvMiZydIVBcIkCTZ1xbhjU4rtmzpIXUWQaa5Q59m3JrgwU0GSYNdIhlzJ04YA755rZ/Ge\n3D/IPXdkiYYDWJazTDyqqVu8e3qBt07MU6mbSBLcc0eWx/f1+/dRu6xdkS+Vurd77le7pkZrc7xU\nM66r79RxXP7xjXHeP5MjFQtQa3gWZNs3p/i1+zfjuC5/89xpFopNdo2k+fJDQ1cs2dZNm8mFKkfO\nLHJi3HMi8BSTJQzTYaQvwZcfHPI3btrOBe+eWqBQ1Xnq4GZ/Q2EpxYrO3zx3hsVyk+HeOE/u3+Tb\n831clGsGrx2d5fBpLwvrqfyrzOUbK651PKKRiAQIBxXOtdwT7t6S4XP3DfitDudnyrxzYoHTk0XC\nAZWH7+7lnjuy/jPq/HSZ//HLsziuyzce28Idg9ce9H7UgNd1Xd44Pscbx2b5tQObuXOo47rfS7AS\nEfDegos0x3GZGi/w+i/Pkc/VUDWZu/b1s2NPH9GITPmlFyn84mfY5RKSppF6/AkyX/0Nzlcn+O6x\nv6Jm1nl88DP8xtanPrZFeUO3GJst8+FYAdNyfOGZ9gLokrqkp5A6MV/l4lz1ihOhLEv0d17yKR3I\nRv1Fj6e26gUzv3p/mtePzeK6sLknzkKhsayxPhkN8NWHh9k1kvFFPUo1g/HZMj9+9QIXZipoqszO\noQ72jHYy2BVDkjwPr7OTZU5dLDA+VyUR1ehtKeH2ZaJkUyEU5ZIaZ0DzSuHkJeerKp7QydWyh7rh\nGaF/cG6Rn715kYZukYhoPLavn10jmWVeqaWqwcRClcn5KhPzNeYKq3s+tif+Nj1pr+dnrBX4wiWr\nlVBA4fP3DfL5+waJhDQc1/XVeC3bRVUk3wqlTbmlCPu3z59edWdfVWR2Dnewb1u2NXYq6XjwquqZ\nH1ewZFr2NYuYCK4PEfBubK51/Cp171nQaGWUg5pCOKgSbwvjrDHv2I5DvWmRLzU5NVnk/HSZifkq\nmUSIe7dn/WcxgKbIdCbDH5tA1kajraTbNGw/wFsqmDXQlyKXq179ja5CW4G22MpevvnhHOBlkb3S\ndYVMIsiOzWk6O8LEwxqhgEK57m00WrbttyAZpuMrp1u2w2BXjNGBlGehgkQoqKDKcktJ2gsy28ru\n7eqeasPTbDg9UeK5tyfIV3S/NHj3SIY7NqW4OF/lRy9foKFb7BpJ89TBzQQ0hUrd4MJMhQvTZT4c\n99YqAVVm77ZO7r+zuyX8JhELa8Qj2nXPF47jUqjoV1UVlpA8S7Ow5meRbcfhF29N8NaJeaJhjS9c\nlm1u6BZ/+/wZphZqjA4keWBXjye41hINrDZMpnN1pnO1Zf3gnckQ+7ZluXtrBtNyeOb1Mc5NlQlq\nCk/uH8R2XN49Nc9s/tLmU0CV+cpDw8sCrdnFOn/7/BmqDZOujjDzhYa/WfDonn4iIdVTwS97G+YN\n3aKvM3rNXrZtP/Wx2Qpjs96Y2a0qsYd293L3lgyKImNatu89P5evU6ot93FPx4M89cBmhntX97hu\n6JYvmnU5F2a8oNd2XL760DB3jaTXffzw0QJe13V59u1J//umyBLffmKUkb4b59X9aUcEvLfYIs22\nbd57Y4LDhy5iWw6Dwx3sObCJrp44xsljzP/197AKBSRVJfHAQ6S//FW0VIpXpw7x/dP/gOu6/MbW\nX+exwYc+lmC32jB568Qcb344x9mp0qqB11qk40E/mM0kQ17JsCwjtyxE+jIRggH1kthFq79q6YOp\nVPMMyC/OV/jxqxcolHVS8SDZVIhsKoztuLz54RyuCw/t7uHz9w2iKTKHPpzj529OoJs2m7tj3vtU\nvdK7TCJEIqoxPlf1A/ZYWKPRKodrI7U8TFOxgF8mrKmyJ6TSsnUIBRS6UmF6M172tZ2FvPQa74E7\nPlvlnw6N8eFYAU2VeXh3L/fv6CagKt6/C6meuFBLYMiyHD97rps2k/Nej2q55YtWqRvUGhbZjjBb\n+xOM9CU8L8NWIH9hpsKZySIX56oM9yb4+iMjdHVErnn8ddNmeqHKi+9PYVpemV9HPEBHzLse7QXY\n5SXUV0IESxsfMYYbm+sZP6PVnxjUVs8Yrod2Ca9lO76SeDsr1Q5GBOvjRn8H24FvqWaA6/qbupIE\nIc3rEb98Y9e0vJahxhUUZ1VZ9pWg13PftPu7TcvGth3G56p0pcLEIsvnl1LN4OmXPHuwdDzoq9+2\niUc07t/Rzb7RTkJBT5W2nTW8UfeZYdrkK56FS9sLXmn5YkeC6orPalsimZa3SbBtqBNTN1Z93x+8\neG6ZsNblBDTZ36C/Y1OKTUs2j8Abz/fO5Hj2rQmM1sa4JHk+3vfe0UVDt/jJa2OYlsODu3p4bG8/\nY7MVfvDiWQzT4cn9g9y/o5szk0WefWuCxbJOKOC1hi2WmyvWgpoq05/1qtHCAQXT9jbV2+uZpVaA\nhuUwu1hflhDJpkIc2NnjWwdejfb9+lGr78ZnK/z3589gWA53jaT5wv5NK6zFLMvxMsVBlaGeS44P\n1xvw2rbDj18b49j5PJ3JEA/t7uWZ18aQZYnffXIbA9mN4ahyqyMC3ltokVar6PzymRNMXSyiBRT2\nHdzEtp3dRKIaub/7IcVnfw6yTPzAQTq/8jW0TCe2Y/PDMz/mlalDhNUQ/2Ln77I9M3rDjsm0HBq6\nyfnpMu+dyfH+2ZxvZ9HXGWXP1gyxsLbMLxA825S2/6YkQV8mRmerFywUUJb17l4rDd1ioehZCcTj\nEarVSzuUsiQxPlfhRy+fp1g16OuMEI8EOHWxSECVefL+TezZmgHgwkyF98/mODHmlf60J4o7NqXo\nSoV91dDpXJ2ZxRoLxSbFqu6f/5WQJM9KoKvD60Pr7oiQ7QiTjgc5N13imdfGqTZMBrJRvvLwMJmE\n5/fWsUrp75UwLRvdvNRbpcjSsr4pWZb8flivh42PnDFxXJdiRadh2LitBaqLiyLLJGMB4tfo1SmC\npY2PGMONjRi/jc+tNIbtsmhcz+LYbSmMt4XUrnUzvt0ffjV/W9t2eP6dSd48MY+mymzuiTPcE2e4\nN0F3S0xLQiIV86zGPq4KOMv25uL1vL/tOCwUPVsa3888oPi9voulJi6eyNbbJxeo65avKaIqMuGg\nQm86sqqYmoQXcDtLLOmKFZ3Xjs0SC2vsHe1c1pc+V6jzgxfOUajo9HdGmcl7+ihffXiYncOXsp3e\nsczz8pEZXBeyHSG6UmG/ImNqocrEfJWF4voVnjviQYZaCZHNPfGPvV/+SiwUG/zk1TGmcjWiIZWn\nDm5m++YOyjWDd08t8O7pBeotl4nBrhiP7u1juDdx1YDXcVzy5SZqy7+3LUDW3swY6Iry7c+OEg6q\nnBwv8MOXzhHUFH7vi9v9EnLXdZnNewJz3R3hq2bSHccTPKw2TS8pkgoR/5iFNl3X5dx0mVyx4dv6\nJaOB6/Zxv1E9zSLgvQUmCNt2OP7eNO++Nk6zYZLtifHgE1vJ9sRxqxVm/vRPaJ49g5JK0fsH/4rI\n6DYATufP8g/nfsp4ZZLuSJZ/ufufk41krumzHdf1xAiKDfJV3ZudcHFciXrT5MPxPCfGCn42NKgp\n7N6S4cDObkb6kl4/0JIeLP+GWXrnSF6pzI2cXEzLYb5QJxYPU600iIQ0YmHVF3mYXazz0zfH+aAl\nyrS5O8ZXHhomm/LsA0zLU220HYemYWFZrr9jLLeUek3LWbWf17IdSi1PR6sloNL2Ra02vGB8vtDw\nxaiW0i4pVmSJR/f2cXBnD+GgSjoe2tCle6spQa6XW2mhJrg+xBhubMT4bXxu9zH0fHrrK+bUdnXV\nUvudetMkGFhpBRZs+affaq0ubQuubDZGs6Yv2/Q2TJuFYmNVO5zLafuGB1sqwe2g27I9ccyrbRiA\nl1D40cvnOdsqf/7W41sYWqM8+GrzfkO3mFqoYdlOq2zdq9hb6sOrqTKqun4P65uF47i8cXyWl96b\nxnZc+jojzC56/cPhoMKerZ0slpucnigBMNQT56kHR0iElWVVia7rMpdv+AJrSzVZwBszx3UZHUjy\nm4+OLLs33z+T4yevjRGPaDy6t5+LsxXOTZeXvUdAk1t+7zHCQZVCRadYNTzV/qrno7s0kgtoMr/9\nxKhvd3WjWSg2+MVbE6tWIyiyRH82ylBrE6o/G10zweM4LkfPL/La0VlKNYO+TIT+rKdd05+Nrhm0\n247D5EKNqYUayWiAvs5Lbici4P0EJwhDtzh2eIoP3pmkUTORZIk9+we458HNaJpK7cPjzP7Zn2JX\nKoTv3EHvH/xL1Fick/nT/OP557hQHgdgV+cOfm/HbxFSV7dfuRzXdSm3eluOnV/k9ESRyYW1d6UC\nqsy2wRR7tnayd1snyWjwIws23QgcxyWVjlIu1lf8zrS8npIjZxeo6zZ7RztJx0PEL/MdbRqeUJXt\nuAQDCuGAsuyB01aVbhoWlu36FjZSy8YGWObh2d7NdloZ1ULLB24uX/eC4GLD76EZ7IqTapl+f5q5\n3RdqnwbEGG5sxPhtfD4NY+i4Lrliw7OPu2y+NkxPbbptPQTeAltuiVCFQ+otbyG31hg6jkuu3KTe\nXL26TEIiHtFIxdZem7Wv3VK9k7VwHJfjF/L0dUbJJNe3rtzItIPO1VgoNvjxq2NM52p0d4S5705P\nVK99300tVHnp/WnOTV0K8NpjkYoFmM3X/Ux3KKAwOpD0Ws1Mz7ZKN2yGeuJ89p6BVcfu0PFZnn17\n0v//aEhlS3+Svs4oc/k643MV8uWVtmWKLHlZ1Ygn2hkNqyiyzNsn5lEUiW9/duuaGxnXQ0O3+NX7\n07x9ch7X9Xrs92ztpNowfQXzfFlfpkGjKrIfwPZ3ej/DQZUPzi3y6gczFKtGy+M4uMKvPBLyPMjb\ndpa243B+uszYbAXDXL45FAmq9GWj/F//+6NrHr8IeD8mGnWDD96e5NjhKQzdRlVlRnd2s+fAIKmO\nCK7rkv/5P7H4938PQPpLXybz61/mdOkCPzn3U8bKEwCMpkb40siTbEkNr/uzDdPm2bcneOHwpJ+1\nlSSvLKMt5d9K8qIoElv7k9y9tfOaS21vFleb5Mt1A8tyrjgR3Ax0w6auW/6ElYoHr8lE/nbm07BQ\nu90RY7ixEeO38RFjuPFZz3rGMGwcWo4YLQuvVGz967Olft1wqaLNaTkg3Kqoigwua7porEYooPp2\nZk3DXvZvVVkmGtZaLXkwm2+saWnYFk+7PGGylItzFU5NlJlZrFKs6JRqhueRLEuMDibZPZJh60Dy\nutbR753JUWuYbOlP0JNeaQtVqRtcnKtiWg4d8SCpeJB4q9Xwck5dLPDDl84jSxLf+uwWtvSt33fY\nMG3OTJY4PpYnX9Z9qzfHhVrDxLAc0vEgn98/yOhActVr1dQtxueqjLX8weeLjWW/VxXZbwnYu62T\nB+7qIRULohs204s1JhdqTC/UmC82fFeQpWQSQUb6EmzqjlOuGUznakzlahSrBs985ytrnttND3j/\n43/8j7z77rtYlsUf/MEf8MILL3D8+HFSKU8e/Pd///d59NFH+clPfsL3vvc9ZFnmm9/8Jt/4xjcw\nTZN/82/+DdPT0yiKwn/4D/+BwcHBq37mzZwgysUG7x26yKljc9iWQzCksnNvH3fvHyTU6tdwDIPZ\nv/gzqu+8jRyN0vsH/4rCYAc/OvtPnCycAWBbagtPDX+erR3rD3TBU6D762dPc2GmjNbK2t4xmGLX\nSJpsKoyqyJ61UMtiCLwdqVvZfkVM8hsfMYYbHzGGGxsxfhsfMYYbn5s1hrWm6bk1aIpvt3UtGeCb\nhYRENKwSC2u+dVS76k43bF8B/HJl7EhQJRkNrmgTMy2vhc3rfV5eWbdWyfy1sLSH13a81rdISL0p\n9mnXwpnJIj944Rxwdd9hy3Y4N1Xm+IU8pyaKvhNIUFP8CgpJ8kTK9m3Lsv/OrmsK6puGxXSuzlTO\nK0NeLDXYOpDigbu6r9prbJg2uVLTt7kb7o2TjK1uc1Zrmjx639Ca73VTR+jQoUOcOXOG73//+xQK\nBb72ta9x4MAB/vAP/5DHHnvMf129XudP/uRPePrpp9E0jd/8zd/kc5/7HC+++CKJRILvfOc7vPrq\nq3znO9/hP/2n/3QzT2FNSoU6b/7qAudPecbZ0XiQPfsHuPPuPrQlX0hjYYHpP/7PGFOTBAYGCfz+\n7/CD8ru8+/YRXFw2xQf4ja1PMdqx5Zo+XzctnnltjGffnsSyHUYHknz90S0MtMzUb+WAViAQCAQC\ngUBwY1itukyWJLKpMIulJtXLSqfbJdMBTSFfbl6X5/B6URWZkKYQWkXVuv37WFhe5gJh2ZdUn5cG\n8ZfjuX6sHkTJskR3R+QjB71tFFkmnbg1y8FHB1L81hNb+f4vz/H9F86xbTDJSG+C4b4E6XgQ1/US\nZMcv5DkxXvSvR0c8yM7hNHcNp2+YD3MooDLSl7gu+6WAptDXGaWvM3rV116tovKmBrz33Xcfu3fv\nBiCRSNBoNLDtlTfdkSNH2LVrF/G413C9b98+Dh8+zBtvvMFXv/pVAB544AH+6I/+6OYd/BqYps3b\nr1zg6DtTrX7TMHsPbmZ0R5fv59qmcBlx4RcAACAASURBVPIYs3/yX1AaOuPb0jx3L9TO/FcAsuEM\nXx75Inu7dvnBaVtsqmnYyLJEJKiu8BUbmynz+vFZ3judY7HcJBJU+fojIzyyp++W23ESCAQCgUAg\nEHwySJJEZyqMXJYo172WN09UM+j3rAY1hYXi+gSwrvp5SAQ0z1GirVp8PSW/vmL1RzweWZboTkeY\nLzRoXsFa63ZgS1+S335iK8+8Ps7J8SInx4sAJKMBTNvxVagTEY292zq5azhNb2ZlOfXtwk2NiBRF\nIRLxfEGffvppPvOZz6AoCn/913/NX/7lX5LJZPi3//bfksvlSKcvyaOn02kWFhaW/b0se4rAhmEQ\nCNx8kQLHcTjz4TyHXjpPvWoQjmo88PhWRnd0rXqznHjj5zjf+z6K7fLivTGOjqqkQxE2RzZzV+cO\nHuq7H0VWMC2bxZLO1GKVucUGi+UG+bLueyGGgirxsIbtuhw5k/Nr4xVZYt9oJ998fPSG7coIBAKB\nQCAQCG4v0omQr6p8eemvpsr0ZiIslpvL1IIVWSYU8Mpc67qFtYaqdFBTiARVggEvE3ureW3LkkR3\nR5hSzaBUNVaUS3/cKLJnWbSWQNmNZKg3wb/++i4KFZ3z02UuzHj/yZLEfduz7BxOM3iZn/PtyieS\nAnz++ed5+umn+Yu/+AuOHTtGKpXizjvv5Lvf/S5//Md/zN69e5e9fq024/W2H2ezN1aae26mzM/+\n/hgXz+eRZIn9Dw/z+Be3EwiuvJxNS+fpH/wXBp9+A9mF3Lce4bef/AoDiV4CrbILx3E4MZbn9aMT\nHDm9wMXZyrq+fpoqs3dblvvv6uH+nT1kkuHb9qa90WMouPmIMdz4iDHc2Ijx2/iIMdz43CpjmL3K\n77u6ElTrBrbjEgmpK6yemrpFtWFSbZioikQsHCAa1lZUIt6qdNFy+yg1qNYvK/GWIBRUsSzH72lt\n05G6enntakiSJ2baEQ8hSTA5X0U3bo6IWEcqysiglzC8UZ63G42bHvC+8sor/Omf/il//ud/Tjwe\n5+DBg/7vHn/8cf7dv/t3PPnkk+RyOf/v5+fn2bNnD11dXSwsLLB9+3ZM08R13XVld2+UQIBhWLz1\n8gWOH57GcVy6+xM88uQ2Ml0xSuXGitefK47xy59+l4dfmsaVJHJf+iqnmqMc/btpHNcrgbYdl4tz\nleVqyt0xujvCpBMh0okgmUSYcEDBdr3g2LZdJBnuGOzwd+Zc0yaXq96Q87zVEEIdGx8xhhsfMYYb\nGzF+Gx8xhhufjTqGRsNY83dR1QuezKZBsbn2625VFCAgeVaeAVUhFPDKr2VcAqpEw3apNEwaTYtU\nKuKLVl1Ou3xbU2UcF2zb8df5oVbZuGNYLC56a3XFsSkW6zc9w3xbM7i2ONdNDXj///buO0iO8s7/\n+Lsn58270opdSRYSEooIZRlMxnCIDAbMcfjH+XxlA4bDJQzls3C5bO6wf/fDcK5zOGPswwGjO7Bs\nwGCCw6EEEkESIkhC0q7C5jCzs5N6+vfHzM7u7M4qIW3S51VFUertmXlmup/wfZ5vd4fDYR566CEe\nf/zx3F2Z77jjDlauXElNTQ0bNmxg6tSpzJ07l6997Wt0dnZit9vZvHkz999/P5FIhD/84Q+cddZZ\nvPrqqyxevHhIym1ZFju2N7L2lZ259OXFZ09m+pzxg86StMXaef7ph7lgbTtpu42Xpl7CW9uCwMEB\n+7qddmZ/ooxZ2QvFiwLuvGfLjYTn4YqIiIiIjDVet2NAanf/v5npNF6/B1Ip0hbZx/VYOOy91ygf\nTfq202GnOOimLRw7/M7ysQ1pwPvcc8/R1tbGXXfdldt29dVXc9ddd+H1evH5fDz44IN4PB7uuece\nbrvtNgzD4Etf+hLBYJBLL72UtWvXcuONN+JyufiXf/mXISn3prV7eP2vuzFsBrPmV7P4U58omL7c\nw0qn2fjT/8dFm9qJ2+08WXURB1JlLJxewcWLJlLkd9FTJywsQj7XgFQREREREREZfnabjaKA+5Cr\n3UeryO8iGkuO6OcjjxVD/hze4fBx0kdam7t46rE3cHscrLhhLmWVgUH37Y6n2PpuPdHf/IjxTfvp\n8Dl4uuLTnDp/Jpctm0hlie+Yy3EyG60pQNJLx3D00zEc3XT8Rj8dw9FPx3B0OxHHL5lKs7+5a0Bq\ns4GhdOejtHD2hEH/pufWHEI6bfHKs++RTlt86tPTDhnsvrOjmd8+s44Ld77I+GSYPeOcHDz7BlYu\n+yTFgzwkWURERERETk5Oh42SkJv2cByPy47H5cg9wiltWbnrgNNpK3ezXgvoWa5MptKkzHTuWcUn\n8hnKo5kC3kPYuqmepgNhJk8rZ/K0we9nV98U5tlf/ZGr9r6My0rxxgwfXRcs4vNzzx/C0oqIiIiI\nyGgS8rkI+QbehNdmGNjsBkdz1WN7JE57JH4cSzc2KOAdRLgjxoY/f4TLbefsi6cNvl80wc9+vZbL\n6l7FYaR59awK3q11suq0K4awtCIiIiIicjIrDrixGQatJ/BmWAYGPo8Dt9OeW302LQvTTJNIpo86\nFXso0rcV8BZgWRavPvceqVSacy46DZ+/8KOPEkmTx377Dmdv/wPedIJ9nz6Td0rruKjmLEo9JUNc\nahEREREROZmFsjfHbek8vkGvzTAIeJ2E/C4c9sLPW7Ysi3jSJJ4wiSVM4kmzYJq1w2bD73US8Dpx\n2A2SqTSJVJp4wiSRMrEs6HvTa7vNwGHPPPap5790mmzKd5pU+tABswLeAt7f2sC+Pe1U1xYzffa4\ngvuY6TTP/HUXtRuepSrRhmPxYp4u20vIGeTTk5TKLCIiIiIiQy/oc2EYBi0dsaNePXXabbicdgwj\nm1adfUyq3+M87KNSDcPA43LgcTkoym5LZVd+k2aaVCqN1+3A58kPQV1OOy6nnYDXecTl7I25D5/z\nrYC3n0Q8xdqXd2B32Dj30tMGfc7umx800fLiC5wf+QhbzSTeWFaB2fARV3ziEtz2wivCIiIiIiIi\nJ1rP6mmkO0l33MRMpwvuZ2DgctrweZz43A6cjsKrt8fKYbcNuiI8VBTw9rNlUz3xWIoFyycSKvYW\n3CeWSPHS6j9xZfMm0r4AE++4nR9t+3d8Dh8Lx50xxCUWERERERHJ17PaChBPmETjKUwzjdNhw+HI\nBKJOu+2wK7ejnQLePpIJk7c31uNy25mzsGbQ/dZu3s3Fda+CYVBz+53ssFroSkb5ZPVi7LajuJWa\niIiIiIjICeZ22XG7Ts44ZXjXl0eYrZv3EY+lmDV/Am7P4HMBB//4Cn4zhvvci/BPm8bGg5sAWDz+\nzKEqqoiIiIiIiByGAt6sVNLkrQ11OJ125i4afHX3g4+aOK3+TVJ2J7WXX0bCTPBO07uUuIuZHJo4\nhCUWERERERGRQ1HAm7Xtzf3EupPMnF+N5xB3CHt3zYsEzW7S85diDwR4p/ldEukEC6vmDXqDKxER\nERERERl6CniBVMrkzQ17cThszFs8+OpuRyTG+PfWYxo2pl1/JQAbDmTSmRcpnVlERERERGREUcAL\nbH/rAN1dSWbMG4/XN/gjhTY9/UdKkmHCU+fhKiklkuzivbYPqfaPY7y/aghLLCIiIiIiIodz0ge8\nppnmzfV7sdsN5i8d/BpcM53Gs/FV0hhM/czVAGxueIe0ldajiEREREREREagkz7g/WBrA12RBNPn\njMfnH3x1d+sLf6W8u5XG6mmUTDwFIHd35oVVCnhFRERERERGmpM+4H1/60EAzlhSe8j9oi+9AMC4\nyy8HoKW7jY869zKlaBIlnuITW0gRERERERE5aid1wBvtSnCwvoPK8UGCRZ5B96t7423KOvazr7iW\naQtmAvB6w5sALBw3f0jKKiIiIiIiIkfnpA54d3/YjGXBlOkVg+5jWRYHf/MbABznXJzb/vrBzdgN\nO2dWzjnh5RQREREREZGjd1IHvDu2NwIwZXrl4Pu89BeKWvexu2giiy9eAsDecD0Ho43MKJ2Gz+kb\nkrKKiIiIiIjI0TlpA95Yd5L9e9sprwoMms5sJhJ0/va/MTEouvJaXE47AK/t2wDA8urFQ1ZeERER\nEREROTonbcCbS2eeMXg685u/eJpgrJPdp8xh4fJZACTMJG80vEXQGWBm2WlDVVwRERERERE5Sidt\nwLvjvSYAppxWOJ050tKOa93LxGwuZt56IzabAcBbTVuImXGWjF+A3WYfsvKKiIiIiIjI0TkpA954\nLMm+3W2UVfgpKvEW3Gfzfz6BJ52gee7Z1E4al9v+v/vWA0pnFhERERERGelOyoB3z44W0mmLKTMK\nr+7Wb99JxYeb6HAFWXrrNbntjdFmdnbsZkrRJCp8ZUNVXBERERERETkGJ2XAuzObzvyJ0wZev2tZ\nFjsefwI7FvaLLsfr710BXrt/IwCfnLBkaAoqIiIiIiIix+ykC3gT8RR1H7VSUuajpGzgI4U2//cf\nqG75iOai8cy7/PzcdjNtsv7gG3jsbuZVzB7KIouIiIiIiMgxOOkC3j07WzBNiynTB67udtbvx/3i\n/5AwHEz+xy9gs/X+PO+2vk84EWFB1Rm47M6hLLKIiIiIiIgcg5Mu4N31fjaduV/Aa6VSfPi9R3Cl\nkzR/8jLGT52U9/fXsunMyycsGpJyioiIiIiIyMdzUgW8iXiKvTtbKSr1Ulruz/vbh0/8mmDbQXaW\nnconb748728d8TDbmrczITCe2uApQ1lkEREREREROUaO4S7AUPrw3QZSqTTTZlZhGEZue3jbNqz/\nfYl2R4BTP/95HPb8eYBX9v6FNJYeRSQiIiIiIjKKnDQrvJZlsW3zfgwDZswdn9tuhsPU/eA/SGNQ\nd9ZVTD21Ku9121re56W6P1PqLmbRuPlDXWwRERERERE5RidNwNuwv5OWpi4mTy3HH3Dntu994gkc\n3RE2jDuTS645O+81rbE2Ht/2S+yGnc/PvgWvwzPUxRYREREREZFjdNIEvO++uR+AmfOrc9uiuz8i\nsWkDDa4Spn/mavye3rsvp9Ip/nPLfxFNdXPt1MupDenaXRERERERkdHkpAh4Y91JdmxvIlTsYcLE\nEiCT4vzef/4MA9h3xgUsnjU+7zVP73iWPeF6FlTN46wJS4ah1CIiIiIiIvJxnBQB7/tbD2KaaWae\nUZ27WdX2l14jcHA3daEaLv/bi/NuYrW58R3+VP8aVb4Kbpp+bd7fREREREREZHQY83dptiyLd9/c\nj81ucNrscQC0d0TpfGY1xRhMvuWz+DyO3L7/u389qz/8HS6bk3+YfQtuu2s4iy8iIiIiIiLHaMwH\nvHt2ttDe2s3UmZV4fS7SaYsXf/zfzIu3E55+JtPnTQcgnIjwxPan2NqyHZ/Dy60zb2Kcv+ow7y4i\nIiIiIiIj1ZgPeDet2wPAzHmZm1U9++cPOPXD10jZncy57WYAtrd8wM+2/5pwIsLU4k9w68wbKXYX\nDVuZRURERERE5OMb8wHv9i0HKCn3kXDa+L9PvkXJG69wmhkjeMkKWl1Jnt/2K15veBO7YePKKZdy\nfu3Z2IyT4tJmERERGaXS6SQ2m/PwO4qInOTGfMCbNi06nTZ+/IPnWdj+LtMje7ECAf44JcrGDf8X\nC4tq/zj+dsb1h3z0kJU2MWz2j10eyzIxjI//PtLLstIAGGNsosKyLMAac99rqFlWWr/hGJepK2m1\nrXJE9d2yTMA2qm9I2dmwlvb9L1M0/lOEqs4a1d9FemmMODSstAmGobHBCTaSxrFjPuBNGi1Mfee3\nnNPRBkBnuZ8X5jnZ37qFav84/mbyhcypmFlwVdeyLLo7PqCz8TUS0QOU1V6Bv3TWMZUjneqmtf55\nou3vEqr6JEXjzhq0UbMs66g7r2N5zYk2FGVKmwkadz5BKtZCoGIBwfJF2J3+E/qZJ0o6nSTRtY94\n117ikTri0Xqw0gTK5hOsXILDNfLS7E/kMf64720mu2ite5bujvdxeMpw+2tx+2twB2pwuEpGXH05\nXizLIh7ZTWfDWmLhXXiLphKqXIY7UDvcRTvu0maCSMubhBvXY1kmZbUr8BZNHe5iASOzTR7tLMsC\ny8zblja7iXfVE4/sJd5VRyJ6EJdvHGUTr8DpqRjwHtH292it+z12ZxFlE6/A5a0cquIfN11t22jf\n/xIAHQf+RDrVTfGEi475fNO5mm84fg/Lsog0baR9/8u4g5Moq12B3Rk87p9xsh5nM9lFvKuuTztx\nAMPmwO2fkBsbuPynYBvBN6rNBI+MmvjATEZo2vVrzGSY0toVeEOnDnkZ+jKsnl9wjHrtimsA+Kja\nxebpPuqrnFQHxh860E2bdLVtobNxLalYMwCG4cCyUpSccgnBioVHVYZYeBcte9ZgJjvBsIGVxuWr\npmzilTg95b37RfbS2fAasfAunJ4K3IHsAN1fg8MVGvT9U/F2mnb9CssyCVYuJVA6F8M2fHMZiegB\nWvb+jlSiHbf/lNx3cPknHFP6VUVFkKam8IDtlpWm+aOn6O54P/e7GoYDf9kZhCqX4HCXHI+vM6hk\nrIVw4zq62t/F7asmVLUcd2DSETcsZjLSrwE+CKRzf3e4S7HSScxkGLDhL51NqHIZTu/AQdxQMVPR\nTFkjdZn/dx/EE5xMac1l2J2BQV832DHsz7IsYp076GxcS6L7ICXVF+IvO+OoG+vujg9o2fs70qku\nHO5SzGQYK53M/d0TOpXySdeO6M7taFlWmu729zK/XXQ/AHZnKNPuAG5/DcGqZXhD046p8zvSYzgU\nzGQX4eaNRJreIG12Z9pn0pkJovIzKa6+cNiObSreTsveNSS7GwhULCRYsQi7wzcsZelrJB2/Y9Hd\n8QGtdc9m28NBGDac7jKSsSYMw0HxhAsIlC/EMAzSZpy2+hfoan0r119g2CmuPp9gxeJREQhUVASp\n/+hdGnb8HMOwUz75Wtr3/ZFkrAl/6VxKa1cc1UqKlTbpOPhnwk2vU1x9LsGKRSew9CNfKt5Gy97f\nkYjux+Wrxh2oyY1fbHb3cfmMQvUwleikde9viYU/yp2bNruX0trL8BXP+NifaVkmrXt/T3fnh5RM\nuBhfyazjcr6byQjhpo1EWt7E4SoiVLkMb/H0EbGa1yNtxmnb9yJdLW/22WrD5RtH2kyQijf3bjbs\n+EtmE6paljc272u42tFUopOmXb8GLMpqr8DlG3fY1+Tqd+N6vEXTCFUtx+Ubf+ILS6YfbNz5BKl4\na25boHwhxRMuOKGXYVRUDD5JNOYD3jeeuxfTGcJWlAkeA8HJBDz5qztWOkUiuj8bfGQG8mmzm0yQ\nMYtg5TKw0jTu/AXpVBdF4885ohQiK52iff/LhJs2AAZF4z9FoHwBbfUvEm17J9shX4jdFaKz4TUS\nXfVAJtBJJTryZrLdgYmU1V4+IIhLdjfRuPOJXFAEaWwOP8GKxQTLF2BzeAqXzTLpOPhXIs2bcLhL\nso16Le5AzTEPziwrTWfDWjoO/gmsNHZXEWaio99e+b+Z3eHH1adTcXmrSMaaM8ciezzc3hCh8Z8e\nUFHb6l8g3LQBd2ASFZOvp6v1bTob12MmOwp+ls3hywbgme/p8o4jGW/tXVHtqsMwbATLF+IvO2PQ\nAXM8up/Ohtfobt+eeV+7N3u+gMtXXbDRtyyLVLw59znxyF5Siba+pcPlG59dgazF7T8FuzPQO/nS\nsDbXMDs9lb2TIYEa7M6igueiZVnEwjszr020U3rKJce0+mVZFvHwR3Q2ZlYMexnYnUHMZGe2c16B\nr3h6wfc4XCdhWSbRtm10NqwlGWvMvHt2kslbNI3SmhVHtHKfNhO07/sjkZZNmcHs+PMIVi4BLJLd\nDcQje4m2v0u8qw6XbwIVU27C7vAezc9x1FLJMIlIHbGuvSQidSRijZnBdh8u7ziCVUvxFZ9+TIOF\nRPQgLXueyf123qLphKqW4fJNIB7Zkzl2nTsAcHoqCFYuw18y66gu0zjRHX0q3kZn4zq6O97HHZiU\nKb+3qsA+6+lqeRPLSmGze3NBpZnozP0GDncpZROvxO0f/DKV482yLLpa36Gt/nmsdCJ3/hqGA3/5\nfEIVS7CsVN4kl2WZBMsXECg/E5u9cFvdw0x2EW7aQKRlM+lUd97fbHY3rsNMLh7N8TNTUSJNrxNp\nfRunu4Rg5TI8wU/ktTNmqptI8+tEmjfh9tdQUnPpCQnsM3X6RSItm8Gw4wnU0rdtN2zObHBSi8tX\njc3mJNq+nda9vydtduMJfoJA+Zm07fsjZqIdp3c85ROvJBlvpbXud6RTUdyBSZRNvGJYs2j6T4Cm\n4m34Sk4nWLkUp7sUgJA/zrvrHyWd6qZiyk14Q1MwU1Gadv6SRHQ/3qLTKJ90zRFNeCe7m2je8zTJ\n7oO5baFxZ1M07lOD9CfpTBuaGyfVY3cGKK29bEA9Pervnuom0afPT8ZbCJTOpWj8OYN+F8uySMaa\n8vpvK53snWQP1ODyjs/28Zn3TXTVARCoWEigbH6uj8/U3bdoq38BK50oMHYxcHor88ZJx3qu9K+H\nXW3baK17FsuM4QlNpaz2MqLt79G+749YVgp/6RyKq8/PjItyk+P7sTuD2bpegydQi91VXPC4pdNJ\nmj96Ktf+A/iKZ2br67H1fcl4K+GGdURa3wLLxLB7sMwYkBm/joSFF4B4ZC/Ne57J1HtPJb6S03H7\na/Pax55J/Hikju6O93IBmrfotMwiRr8+ZDgC3mSsJTPO7zknDRvF488lWLl00PFC3/rd0xcBeIKT\nCVUux+mtymTGdGXbm1hLJig+DosqfeOSUNUn8RXPyPbNTTjcZZRNugq3r7rga1OJdjob1xNt25o5\n98efd1Tn0ZgLeL/97W/z9ttvYxgG999/P3PmzBl0322vfZdYV0OfLQZGv0DGSifzBqB2VxG+oukD\n0kj7nnTBisUDUogyDXBjn6BmN2YynDnAE6/E7Z+Q2zfa9i6tdc/mAiUAT2gqoarleAK12SD8APGu\nvXR37iQe2Y1hc1FyysX4S+dhGAbxrn007fwlabOb4uoL8JfOprNxA5HmTVjpOIbNRaD8TIIVi/NW\niJOxZlr2PEMiuh/D7sYyE0DvaWDY3P1jxQHsjkCf4KwGw7DRsucZ4l112J1BSmsvz3TGuU68jkT3\ngbzf2cLCjLdjpiKDfk4umDRsFI07h1DVMgzDRrjpddrqn8fhKWfc1P+TC+wzgdO7dLW+nbeiB5BK\ndORWuwb7LCudHDCQttKJvEC1J6hwescTqlqGr3gGieh+OhvW0t3xXvY3dGZmanukzVyDA2DY3bnB\n6ZGsfluWRXfnB0SaNhKP1OW9V0/H13MsnJ4Kou3b6WxcS7K759w3AItA+QKKJ1x4RDNslpUm2r6d\ncMPazLEjs0roDk7Gk+00DJuLSPPrtO97Kds5z6NkwoWkEm3EsgOMeFc9WAnSh2pqcr+Pga9kJqHK\nZdgcXlr2/JZ4ZDc2h4/S2hW4/TV5g8JkrJm+527P+zg9lZRNuqrgQMyyTFr2rCHatgWnp5LKUz+b\nSx2z0im6Wt8h3LSRVDJ/ssYwHJmBdXYw5fZNwEoneydnuupIdjdi9S2PZWGlE33exI7LU5nXgFuW\nSSJ6ALCwu4oJVS7FXzYPM9GePe/2Eu/ah93pJ1ixFG9R7wqtZaUJN66j/cCrYKXxl84lVLW84Ox0\noruBzoa1RNu2Zj7LGSJYuSRv4NdfKtFBuHF9ZmWgagbO4Jk4XMUF9+lq25p3XkJmQit3ngdqcLjL\nBwzKEtEDmXK1vwtYGDZnru56QqdmzgW7h87GtUTbtmXLXkSocsmAiSkrnaL9wKuEG9dlf+5Dr8oY\nGNkJpD4rOccwCDRT0cwKSsd72Xb6EnzFM7Lp1usKtjuGzQ1kzg/D5iZQfiahysUD0hiT8dZMJknL\n25m2yeHD6S7r9/ldeTPphfo5h8ODw1vd2+b4xg24rKbnWEZaNmOlk3nHoqe9c/sm5ALvzN8ybYvN\nEaBs4uVHnLY2IICK7sNm9+RlNpnJzCRGKt6K01NF2aQrjzi4MpMRWvau6TPQNwhVLc8EdNmJnsw+\nvyPW+SGGzYU7MPGQ9TsVb8Pprcq1tz0B9rHKTfQ1rs8LPDFs2Owe0qkoYOArnkGg/Ew69v+BeLSJ\n0prLCJTPz+2eNuM07XqSeGQ3dlcxnuDk3uwwd+mAcUq4aWMmJdoy8ZfOI1ixiKaPfoOZaCdQsYiS\nCRfnXpM7J1rfwjLjuffp7Zvt2cH3ktzgu+dysHDTOsxkV17aqMNTTirRlutTE111JGNNfX4VIzsu\nieH0VlE28aq8tPPcqmLzprzxk83uxbA5D9PHe7AsEyudxGb3EKhYiL94Fu0HXqG7430Mu5vSUy7F\nVzILy4zlBQSJrv35fbjNBXltmYHTU54XFNtsLhLdB/p8131AqrcvtMiO1ZwUT7iIQNn83O/ed5zW\nn8NVgpmK5I1x7K5ighWL8trzdCpG065fEe+qwxOcQvGEC2ite5ZEVz12Z5Cy2itweiv7TGLsJRlv\nJa9PLaDnPHC4SghWLsVfNhcz0Uln4zq6Wt8GyzzChZc0ye7G3KRFIrofh7s0084cRbbcgPdNm3Qc\n/BOdDWsBCFUto2jcOYed4LWsNN0d72cWoLK/e//xsN3uxOEZlzd2wzLzLqtIJTrwFZ9esD0v9JnR\ntm2EmzZi2OwEK5bgLTot990T0YN5i20uXzUte9aQTkVw+2som3hl3kLYgPpddgYlEy4i3lVPZ8Nr\nxCO7C5TCwObwkU51AeANTcuscnvHkYjuyxvDW/0uKenfx6fNOE07f5WLS0JVy4DMxEvH/ldyC4C9\nizaZBZ5UoiNvfNLTrxQay2UmfzcSbX8Xt7+GUNXS3OUrYyrg3bhxIz/5yU/44Q9/yM6dO7n//vt5\n8sknD/magwcae2cPu+pI92m0ITuQ9U84svThRGf2mtFmbA5/3uxK2oznDW5tdi/+0jkUVZ9XsFNM\nJcO017+IYXMQrFw66LVElmURNint6AAAFlFJREFUbdtCa93zWOk43qLT8JfMpmXvGqx0ktLaywiU\nndGnHDEizZvobNxAOhUBw4a/ZA6hqqXEwnto3/cilpXCVzKH0ppPAzYS0fpco2xmT/pBWZBKtOUP\n5LN8xadTUvM3RzxzaFlWZmDfU6FiDTjd5dkBaC0Odylu2wE+eufXmNkK7i+dQ2vdc9gcPsZN+z9H\nlbqcSnT0zpJ2H8TpLs3riNOpKOHm14k0vZ7tTDOVrodhOHAHJxGqWII7OHlAg5yMNdPZuJ5EdF+/\nTzYyaerZRsHpqfxYjXmi+2DezHY675j1lNnINLpVywCjz+pXWTadvizbSGc6uvwBc2YiKJ2dtfUW\nz8hcB9pn0ib/ezfRvPsZktnAuC+bI4DbGySVShd4ZW+Z3f6aAanomcZ7A+37Xx5w7R4YODxl/Qbt\nBt7QqRSNO/uQs4KWZdG27wUiTRuxu4qpmHwdsfCuPnXGjtNdntfRpc1Y/qx/T1pkHw532YDPtTtD\neAK1uPw1uH3VBcvVE9REWt7Kfs9+553NjZXODjI85dljUUPr3t8R79p7VMFGzwxqV8ubmaDG7sYT\nmNxvVaQl2/lsyZYjkz2SmZCYlTunwo3r6GrdQk9mSV5auwWpZHveINmwu7HZ+gRilpWb8HJ6qwhV\nLsdXMoNYZyYzId61N6/sTk8loarl+EpOP+SNXWLh3ZlrG9PxQfeBTF1KxVvo+1vbHYF+A9nDS5ux\n7OpSbXYA0jsp0BPURFrewu7snSh0eiqwzATh5jcIN23I1GHDht2Rn8lgJiP0nwwp1J/0nVyMR+sH\nTPhZZpRUondlwjAcA4L73Gf1mQxJxpsJ5yYketmdQYIViwmUzSfSsik36RIoX0Bx9QWYyXDe/Qis\nfv1uz2/Ww+bwZfrmAfUcgpXLKD7Eat9gLMsi0rKJ7vb3KBr3KdyBmoL7dLW8SWfDa/lZNwXqt90R\nyJ+gLXC8CrE7Q/kTxDZn5jNzGUkGnuAnMgF3oAaXrxrDsBNtfzeT9dInGA5WLqNkwgUDv0c6lblP\nSNu2fuMQT2YCNvd9TdKpaGYSsWYFvuLTgMx4pGnHL0jGGvGVzCZUuZRw04Zc/bY7AniKpuYF0rHO\nHbTsXUM61YU7UEtpzQriXXvpbFiXy0gybK4Bk359j3FmhX5Cn4mOUwAjb1W/uPo8vKFpucA7E1D5\n8IamDphMy/TxPZfcHMDhKsUTyKyEOj0VpM1uIk2vE27amBcwH26VP9PvHugT1LTn/90yScXy25L+\n39XuDOHy+PP6QoeziOIJF+L05E9i9bxn5jK33ZkMsOxvZHf4ssHiQWLZ/jvWuaM3kC9fiK9kZqbP\n7z6Ir/h0yiZehWGzZzPxXqPjwJ/pewlV9mzJl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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "8T0yfWPw-7QZ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Example 2: Train an agent built from scratch.\n", + "The purpose of this example is to demonstrate how one can create an agent from scratch. The agent\n", + "created here is meant to demonstrate the bare minimum functionality that is expected from agents. It is\n", + "selecting actions in a very suboptimal way, so it will clearly do poorly." + ] + }, + { + "metadata": { + "id": "1kgV__YU-_ET", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Create a completely new agent from scratch.\n", + "\n", + "LOG_PATH = os.path.join(BASE_PATH, 'sticky_agent', GAME)\n", + "\n", + "class StickyAgent(object):\n", + " \"\"\"This agent randomly selects an action and sticks to it. It will change\n", + " actions with probability switch_prob.\"\"\"\n", + " def __init__(self, sess, num_actions, switch_prob=0.1):\n", + " self._sess = sess\n", + " self._num_actions = num_actions\n", + " self._switch_prob = switch_prob\n", + " self._last_action = np.random.randint(num_actions)\n", + " self.eval_mode = False\n", + " \n", + " def _choose_action(self):\n", + " if np.random.random() <= self._switch_prob:\n", + " self._last_action = np.random.randint(self._num_actions)\n", + " return self._last_action\n", + " \n", + " def bundle_and_checkpoint(self, unused_checkpoint_dir, unused_iteration):\n", + " pass\n", + " \n", + " def unbundle(self, unused_checkpoint_dir, unused_checkpoint_version,\n", + " unused_data):\n", + " pass\n", + " \n", + " def begin_episode(self, unused_observation):\n", + " return self._choose_action()\n", + " \n", + " def end_episode(self, unused_reward):\n", + " pass\n", + " \n", + " def step(self, reward, observation):\n", + " return self._choose_action()\n", + " \n", + "def create_sticky_agent(sess, environment, summary_writer=None):\n", + " \"\"\"The Runner class will expect a function of this type to create an agent.\"\"\"\n", + " return StickyAgent(sess, num_actions=environment.action_space.n,\n", + " switch_prob=0.2)\n", + "\n", + "sticky_config = \"\"\"\n", + "import dopamine.discrete_domains.atari_lib\n", + "import dopamine.discrete_domains.run_experiment\n", + "atari_lib.create_atari_environment.game_name = '{}'\n", + "atari_lib.create_atari_environment.sticky_actions = True\n", + "run_experiment.Runner.num_iterations = 200\n", + "run_experiment.Runner.training_steps = 10\n", + "run_experiment.Runner.max_steps_per_episode = 100\n", + "\"\"\".format(GAME)\n", + "gin.parse_config(sticky_config, skip_unknown=False)\n", + "\n", + "# Create the runner class with this agent. We use very small numbers of steps\n", + "# to terminate quickly, as this is mostly meant for demonstrating how one can\n", + "# use the framework.\n", + "sticky_runner = run_experiment.TrainRunner(LOG_PATH, create_sticky_agent)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "gQt3t_IS_Gku", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Train StickyAgent.\n", + "print('Will train sticky agent, please be patient, may be a while...')\n", + "sticky_runner.run_experiment()\n", + "print('Done training!')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "oom0wB0A_Qb8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Load the training logs.\n", + "sticky_data = colab_utils.read_experiment(log_path=LOG_PATH, verbose=True)\n", + "sticky_data['agent'] = 'StickyAgent'\n", + "sticky_data['run_number'] = 1\n", + "experimental_data[GAME] = experimental_data[GAME].merge(sticky_data,\n", + " how='outer')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DqsagPbb_Xjm", + "colab_type": "code", + "outputId": "1d263334-e476-4f76-88df-28d0b6a271ae", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 512 + } + }, + "cell_type": "code", + "source": [ + "# @title Plot training results.\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(figsize=(16,8))\n", + "sns.tsplot(data=experimental_data[GAME], time='iteration', unit='run_number',\n", + " condition='agent', value='train_episode_returns', ax=ax)\n", + "plt.title(GAME)\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Bfy0GVcVVV48RDGIE7bJnMx7H0nWsRG6Tqp5meTP3/nbFiETS5dZWIpG+vpXRsMpVX9/l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x5z0oxQhMwsjoZZl+psLZBnqpLncvEqKS1JUFXIVy1GM6OMzZ3n5fNv4i0lAevdVAoenlKitT1J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ntmKZNvfuznH76nRpg0ZRpYoKci6rc/WTcWRZ5PDTlZtqnydqiZuVhCJeLMtGFAQEUUAUBVKJ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WweTHLbpixHz5U4MVlmqCfG73xuK5FQgHQ6SqFY5+1j87x4YAbbcXn4jn4e2T2ALMn0ZCIE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\nJ9FFMzzoWqXVRqFIlSsVt/aAl1vbYqyNhRb8rJJTCVB3T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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] } \ No newline at end of file diff --git a/dopamine/colab/cartpole.ipynb b/dopamine/colab/cartpole.ipynb index a9534d5..8a0c361 100644 --- a/dopamine/colab/cartpole.ipynb +++ b/dopamine/colab/cartpole.ipynb @@ -1,342 +1,342 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "cartpole.ipynb", - "version": "0.3.2", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - } - }, - "cells": [ - { - "metadata": { - "id": "VYNA79KmgvbY", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Copyright 2019 The Dopamine Authors.\n", - "\n", - "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", - "\n", - "https://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." - ] - }, - { - "metadata": { - "id": "emUEZEvldNyX", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# Dopamine: How to train an agent on Cartpole\n", - "\n", - "This colab demonstrates how to train the DQN and C51 on Cartpole, based on the default configurations provided.\n", - "\n", - "The hyperparameters chosen are by no mean optimal. The purpose of this colab is to illustrate how to train two\n", - "agents on a non-Atari gym environment: cartpole.\n", - "\n", - "We also include default configurations for Acrobot in our repository: https://github.com/google/dopamine\n", - "\n", - "Run all the cells below in order." - ] - }, - { - "metadata": { - "id": "Ckq6WG-seC7F", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Install necessary packages.\n", - "!pip install --upgrade --no-cache-dir dopamine-rl\n", - "!pip install gin-config" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "WzwZoRKxdFov", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Necessary imports and globals.\n", - "\n", - "import numpy as np\n", - "import os\n", - "from dopamine.discrete_domains import run_experiment\n", - "from dopamine.colab import utils as colab_utils\n", - "from absl import flags\n", - "import gin.tf\n", - "\n", - "BASE_PATH = '/tmp/colab_dopamine_run' # @param" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "bidurBV0djGi", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "## Train DQN" - ] - }, - { - "metadata": { - "id": "PUBRSmX6dfa3", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Load the configuration for DQN.\n", - "\n", - "DQN_PATH = os.path.join(BASE_PATH, 'dqn')\n", - "# Modified from dopamine/agents/dqn/config/dqn_cartpole.gin\n", - "dqn_config = \"\"\"\n", - "# Hyperparameters for a simple DQN-style Cartpole agent. The hyperparameters\n", - "# chosen achieve reasonable performance.\n", - "import dopamine.discrete_domains.gym_lib\n", - "import dopamine.discrete_domains.run_experiment\n", - "import dopamine.agents.dqn.dqn_agent\n", - "import dopamine.replay_memory.circular_replay_buffer\n", - "import gin.tf.external_configurables\n", - "\n", - "DQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n", - "DQNAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n", - "DQNAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n", - "DQNAgent.network = @gym_lib.cartpole_dqn_network\n", - "DQNAgent.gamma = 0.99\n", - "DQNAgent.update_horizon = 1\n", - "DQNAgent.min_replay_history = 500\n", - "DQNAgent.update_period = 4\n", - "DQNAgent.target_update_period = 100\n", - "DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon\n", - "DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version\n", - "DQNAgent.optimizer = @tf.train.AdamOptimizer()\n", - "\n", - "tf.train.AdamOptimizer.learning_rate = 0.001\n", - "tf.train.AdamOptimizer.epsilon = 0.0003125\n", - "\n", - "create_gym_environment.environment_name = 'CartPole'\n", - "create_gym_environment.version = 'v0'\n", - "create_agent.agent_name = 'dqn'\n", - "TrainRunner.create_environment_fn = @gym_lib.create_gym_environment\n", - "Runner.num_iterations = 50\n", - "Runner.training_steps = 1000\n", - "Runner.evaluation_steps = 1000\n", - "Runner.max_steps_per_episode = 200 # Default max episode length.\n", - "\n", - "WrappedReplayBuffer.replay_capacity = 50000\n", - "WrappedReplayBuffer.batch_size = 128\n", - "\"\"\"\n", - "gin.parse_config(dqn_config, skip_unknown=False)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "WuWFGwGHfkFp", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Train DQN on Cartpole\n", - "dqn_runner = run_experiment.create_runner(DQN_PATH, schedule='continuous_train')\n", - "print('Will train DQN agent, please be patient, may be a while...')\n", - "dqn_runner.run_experiment()\n", - "print('Done training!')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "aRkvG1Nr6Etc", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# Train C51" - ] - }, - { - "metadata": { - "id": "s5o3a8HX6G2A", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Load the configuration for C51.\n", - "\n", - "C51_PATH = os.path.join(BASE_PATH, 'c51')\n", - "# Modified from dopamine/agents/rainbow/config/c51_cartpole.gin\n", - "c51_config = \"\"\"\n", - "# Hyperparameters for a simple C51-style Cartpole agent. The hyperparameters\n", - "# chosen achieve reasonable performance.\n", - "import dopamine.agents.dqn.dqn_agent\n", - "import dopamine.agents.rainbow.rainbow_agent\n", - "import dopamine.discrete_domains.gym_lib\n", - "import dopamine.discrete_domains.run_experiment\n", - "import dopamine.replay_memory.prioritized_replay_buffer\n", - "import gin.tf.external_configurables\n", - "\n", - "RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n", - "RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n", - "RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n", - "RainbowAgent.network = @gym_lib.cartpole_rainbow_network\n", - "RainbowAgent.num_atoms = 51\n", - "RainbowAgent.vmax = 10.\n", - "RainbowAgent.gamma = 0.99\n", - "RainbowAgent.update_horizon = 1\n", - "RainbowAgent.min_replay_history = 500\n", - "RainbowAgent.update_period = 4\n", - "RainbowAgent.target_update_period = 100\n", - "RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon\n", - "RainbowAgent.replay_scheme = 'uniform'\n", - "RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version\n", - "RainbowAgent.optimizer = @tf.train.AdamOptimizer()\n", - "\n", - "tf.train.AdamOptimizer.learning_rate = 0.001\n", - "tf.train.AdamOptimizer.epsilon = 0.0003125\n", - "\n", - "create_gym_environment.environment_name = 'CartPole'\n", - "create_gym_environment.version = 'v0'\n", - "create_agent.agent_name = 'rainbow'\n", - "Runner.create_environment_fn = @gym_lib.create_gym_environment\n", - "Runner.num_iterations = 50\n", - "Runner.training_steps = 1000\n", - "Runner.evaluation_steps = 1000\n", - "Runner.max_steps_per_episode = 200 # Default max episode length.\n", - "\n", - "WrappedPrioritizedReplayBuffer.replay_capacity = 50000\n", - "WrappedPrioritizedReplayBuffer.batch_size = 128\n", - "\"\"\"\n", - "gin.parse_config(c51_config, skip_unknown=False)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "VI_v9lm66jzq", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Train C51 on Cartpole\n", - "c51_runner = run_experiment.create_runner(C51_PATH, schedule='continuous_train')\n", - "print('Will train agent, please be patient, may be a while...')\n", - "c51_runner.run_experiment()\n", - "print('Done training!')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "hqBe5Yad63FT", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# Plot the results" - ] - }, - { - "metadata": { - "id": "IknanILXX4Zz", - "colab_type": "code", - "outputId": "e7e5b94c-2872-426b-fb69-a51365eb5fe4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "cell_type": "code", - "source": [ - "# @title Load the training logs.\n", - "data = colab_utils.read_experiment(DQN_PATH, verbose=True,\n", - " summary_keys=['train_episode_returns'])\n", - "data['agent'] = 'DQN'\n", - "data['run'] = 1\n", - "c51_data = colab_utils.read_experiment(C51_PATH, verbose=True,\n", - " summary_keys=['train_episode_returns'])\n", - "c51_data['agent'] = 'C51'\n", - "c51_data['run'] = 1\n", - "data = data.merge(c51_data, how='outer')" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Reading statistics from: /tmp/colab_dopamine_run/dqn//logs/log_49\n", - "Reading statistics from: /tmp/colab_dopamine_run/c51//logs/log_49\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "mSOVFUKN-kea", - "colab_type": "code", - "outputId": "8ec8c20a-2409-420e-a0a0-1b14907bfbed", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 512 - } - }, - "cell_type": "code", - "source": [ - "# @title Plot training results.\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,8))\n", - "sns.tsplot(data=data, time='iteration', unit='run',\n", - " condition='agent', value='train_episode_returns', ax=ax)\n", - "plt.title('Cartpole')\n", - "plt.show()" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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gldQhEZGdGFitk2bFXd0j5eupwcQIH5RVt6G+RTfkz9P3mnCwuBH+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\n6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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - } - ] +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "cartpole.ipynb", + "version": "0.3.2", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "cells": [ + { + "metadata": { + "id": "VYNA79KmgvbY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Copyright 2019 The Dopamine Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", + "\n", + "https://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + ] + }, + { + "metadata": { + "id": "emUEZEvldNyX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Dopamine: How to train an agent on Cartpole\n", + "\n", + "This colab demonstrates how to train the DQN and C51 on Cartpole, based on the default configurations provided.\n", + "\n", + "The hyperparameters chosen are by no mean optimal. The purpose of this colab is to illustrate how to train two\n", + "agents on a non-Atari gym environment: cartpole.\n", + "\n", + "We also include default configurations for Acrobot in our repository: https://github.com/google/dopamine\n", + "\n", + "Run all the cells below in order." + ] + }, + { + "metadata": { + "id": "Ckq6WG-seC7F", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Install necessary packages.\n", + "!pip install --upgrade --no-cache-dir dopamine-rl\n", + "!pip install gin-config" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WzwZoRKxdFov", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Necessary imports and globals.\n", + "\n", + "import numpy as np\n", + "import os\n", + "from dopamine.discrete_domains import run_experiment\n", + "from dopamine.colab import utils as colab_utils\n", + "from absl import flags\n", + "import gin.tf\n", + "\n", + "BASE_PATH = '/tmp/colab_dopamine_run' # @param" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bidurBV0djGi", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Train DQN" + ] + }, + { + "metadata": { + "id": "PUBRSmX6dfa3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Load the configuration for DQN.\n", + "\n", + "DQN_PATH = os.path.join(BASE_PATH, 'dqn')\n", + "# Modified from dopamine/agents/dqn/config/dqn_cartpole.gin\n", + "dqn_config = \"\"\"\n", + "# Hyperparameters for a simple DQN-style Cartpole agent. The hyperparameters\n", + "# chosen achieve reasonable performance.\n", + "import dopamine.discrete_domains.gym_lib\n", + "import dopamine.discrete_domains.run_experiment\n", + "import dopamine.agents.dqn.dqn_agent\n", + "import dopamine.replay_memory.circular_replay_buffer\n", + "import gin.tf.external_configurables\n", + "\n", + "DQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n", + "DQNAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n", + "DQNAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n", + "DQNAgent.network = @gym_lib.cartpole_dqn_network\n", + "DQNAgent.gamma = 0.99\n", + "DQNAgent.update_horizon = 1\n", + "DQNAgent.min_replay_history = 500\n", + "DQNAgent.update_period = 4\n", + "DQNAgent.target_update_period = 100\n", + "DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon\n", + "DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version\n", + "DQNAgent.optimizer = @tf.train.AdamOptimizer()\n", + "\n", + "tf.train.AdamOptimizer.learning_rate = 0.001\n", + "tf.train.AdamOptimizer.epsilon = 0.0003125\n", + "\n", + "create_gym_environment.environment_name = 'CartPole'\n", + "create_gym_environment.version = 'v0'\n", + "create_agent.agent_name = 'dqn'\n", + "TrainRunner.create_environment_fn = @gym_lib.create_gym_environment\n", + "Runner.num_iterations = 50\n", + "Runner.training_steps = 1000\n", + "Runner.evaluation_steps = 1000\n", + "Runner.max_steps_per_episode = 200 # Default max episode length.\n", + "\n", + "WrappedReplayBuffer.replay_capacity = 50000\n", + "WrappedReplayBuffer.batch_size = 128\n", + "\"\"\"\n", + "gin.parse_config(dqn_config, skip_unknown=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WuWFGwGHfkFp", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Train DQN on Cartpole\n", + "dqn_runner = run_experiment.create_runner(DQN_PATH, schedule='continuous_train')\n", + "print('Will train DQN agent, please be patient, may be a while...')\n", + "dqn_runner.run_experiment()\n", + "print('Done training!')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "aRkvG1Nr6Etc", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Train C51" + ] + }, + { + "metadata": { + "id": "s5o3a8HX6G2A", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Load the configuration for C51.\n", + "\n", + "C51_PATH = os.path.join(BASE_PATH, 'c51')\n", + "# Modified from dopamine/agents/rainbow/config/c51_cartpole.gin\n", + "c51_config = \"\"\"\n", + "# Hyperparameters for a simple C51-style Cartpole agent. The hyperparameters\n", + "# chosen achieve reasonable performance.\n", + "import dopamine.agents.dqn.dqn_agent\n", + "import dopamine.agents.rainbow.rainbow_agent\n", + "import dopamine.discrete_domains.gym_lib\n", + "import dopamine.discrete_domains.run_experiment\n", + "import dopamine.replay_memory.prioritized_replay_buffer\n", + "import gin.tf.external_configurables\n", + "\n", + "RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n", + "RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n", + "RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n", + "RainbowAgent.network = @gym_lib.cartpole_rainbow_network\n", + "RainbowAgent.num_atoms = 51\n", + "RainbowAgent.vmax = 10.\n", + "RainbowAgent.gamma = 0.99\n", + "RainbowAgent.update_horizon = 1\n", + "RainbowAgent.min_replay_history = 500\n", + "RainbowAgent.update_period = 4\n", + "RainbowAgent.target_update_period = 100\n", + "RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon\n", + "RainbowAgent.replay_scheme = 'uniform'\n", + "RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version\n", + "RainbowAgent.optimizer = @tf.train.AdamOptimizer()\n", + "\n", + "tf.train.AdamOptimizer.learning_rate = 0.001\n", + "tf.train.AdamOptimizer.epsilon = 0.0003125\n", + "\n", + "create_gym_environment.environment_name = 'CartPole'\n", + "create_gym_environment.version = 'v0'\n", + "create_agent.agent_name = 'rainbow'\n", + "Runner.create_environment_fn = @gym_lib.create_gym_environment\n", + "Runner.num_iterations = 50\n", + "Runner.training_steps = 1000\n", + "Runner.evaluation_steps = 1000\n", + "Runner.max_steps_per_episode = 200 # Default max episode length.\n", + "\n", + "WrappedPrioritizedReplayBuffer.replay_capacity = 50000\n", + "WrappedPrioritizedReplayBuffer.batch_size = 128\n", + "\"\"\"\n", + "gin.parse_config(c51_config, skip_unknown=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VI_v9lm66jzq", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Train C51 on Cartpole\n", + "c51_runner = run_experiment.create_runner(C51_PATH, schedule='continuous_train')\n", + "print('Will train agent, please be patient, may be a while...')\n", + "c51_runner.run_experiment()\n", + "print('Done training!')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hqBe5Yad63FT", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Plot the results" + ] + }, + { + "metadata": { + "id": "IknanILXX4Zz", + "colab_type": "code", + "outputId": "e7e5b94c-2872-426b-fb69-a51365eb5fe4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "cell_type": "code", + "source": [ + "# @title Load the training logs.\n", + "data = colab_utils.read_experiment(DQN_PATH, verbose=True,\n", + " summary_keys=['train_episode_returns'])\n", + "data['agent'] = 'DQN'\n", + "data['run'] = 1\n", + "c51_data = colab_utils.read_experiment(C51_PATH, verbose=True,\n", + " summary_keys=['train_episode_returns'])\n", + "c51_data['agent'] = 'C51'\n", + "c51_data['run'] = 1\n", + "data = data.merge(c51_data, how='outer')" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Reading statistics from: /tmp/colab_dopamine_run/dqn//logs/log_49\n", + "Reading statistics from: /tmp/colab_dopamine_run/c51//logs/log_49\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "mSOVFUKN-kea", + "colab_type": "code", + "outputId": "8ec8c20a-2409-420e-a0a0-1b14907bfbed", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 512 + } + }, + "cell_type": "code", + "source": [ + "# @title Plot training results.\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(figsize=(16,8))\n", + "sns.tsplot(data=data, time='iteration', unit='run',\n", + " condition='agent', value='train_episode_returns', ax=ax)\n", + "plt.title('Cartpole')\n", + "plt.show()" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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gldQhEZGdGFitk2bFXd0j5eupwcQIH5RVt6G+RTfkz9P3mnCwuBH+\nXhpZTFsmInJUGrUSRpMZRtPpJ/7S0DCJtQM/5Q4MdBpeaxidnZNKibsXp0CjVmJ7drXU4RCRHdAb\nTCisbMY4fzfZrnOZlRwCYHjV2KySRugNJsxICobCztaoERHZkxNrdliNHQ0msTJnMJqwO68Wnq5O\nspqC6Si83J0RE+qF+mYd2nW9UodDRDJ3uLIFvUYz0mQ8YG9yXACcnZTYW1AH8xCPSuzO72slHphw\nTERE1qFR940k6tHbz3AnOWISK3OZxX0DnWalhnCgk5XEhHkBAI5Ut0kcCRHJXU5ZIwB5rdb5NWe1\nEhnxAdC29aD0eOs5n9/c3oPDlS2ICfVCkEyry0REjkLjzEqsJTArkrkd/bthL+BAJ6uJDe1LYsuq\nmMQS0ZmZRRG5ZU3wcHVCVIin1OGc1cyUvpbioeyM3VtQBxGswhIR2QLbiS2DSayM1TZ1ofh4KxIj\nfHh33Ioix3lCIQgoYyWWiM6isrYDbV29mBTtD4VC3udG48O94efpjINFDdAbzvxGSRRF7Mmvg0qp\nwHkJgTaMkIhobBpsJ7ajXbFyxCRWxjjQyTY0ahXGB7qjorYDBiMnxRHR6dlDK/EAhSBgRnIwenpN\nyC5pPOPzKus6UNukQ3qsP1xltPOWiMhRsRJrGUxiZcpgNGN3Xh08XJ2QbgdvmOxdTKgXjCYzjtZ3\nSB0KEclUTmkTVEoFkiJ8pQ5lSGYk9bUH7zlLS/HABOOZ3A1LRGQTLv2V2G5WYkeFSaxMZZY0oLPb\ngPNTONDJFgaGO/FcLBGdjra1G1WNnUiM8IFz/110uQvxc0PUOE8UVDajpUN/yuNGkxn7D9fD09UJ\nSZH2kZgTEdk7VmItg9mRTP3EgU42FTuQxPJcLBGdRk6ZFgBkvVrndGYlB0MUgX2Fp1ZjDx1pQme3\nAdOTgnmzlIjIRjid2DL4W0uG6pp1KDrWiokTfBDky4FOtuDrqYGPhzPKqlohDnGvIhGNHbn9Sewk\nO0tip04MgkopYE9e3Sk/2wbajNlKTERkOxzsZBlMYmVooAp7YRqrsLYUG+aFdp0Bja3dUodCRDKi\n6zGi6FgrJgR7wMfDWepwhsXdxQmTYvxRre3CsfrOwY93dhuQW6ZFWIA7woM8JIyQiGhsYTuxZTCJ\nlRmD0YxdebVwd3FCemyA1OGMKdH9+2JLeS6WiH4hv6IJJrOIdDurwg4YqLTuzq8d/Nj+wnqYzCKr\nsERENjaYxOqZxI4Gk1iZyS5tHBzo5KTiX48tDZyLPcJzsUT0C/baSjwgJcoP7i5O2F9YD6Opb43Y\nnvw6CAIwPSlI4uiIiMYWthNbBrMkmdkxMNCJrcQ2Nz7QHWonBUqZxBJRP5PZjENHmuDj4YzwIHep\nwxkRlVKB6YlB6NAZkF/ejNqmLlTUtiM50g/e7vbVHk1EZO/YTmwZTGJlxGgyo+hoCyJDPBHMgU42\np1QoEBXiiZrGLuh6DFKHQ0QyUFbVhq4eI9Ji/SEIgtThjNjMlIGdsbUc6EREJCGVUgGVUsFK7Cgx\niZWRbr0RImB3g0McSUyYN0QAR2rapQ6FiGQgu7Svldhez8MOmBDkgVB/N+SUabErrxYuzkqkx9r3\n90REZK80aiUrsaOksuaLl5SU4I477sBNN92EFStW4J577kFLSwsAoLW1FWlpafjDH/6AK6+8EsnJ\nyQAAHx8fvPTSS9YMS7a69X13ZFydrfrXQmcR84vhTilRfhJHQ0RSEkUROWVaOKuViA/3kTqcUREE\nATOTg/Hpj0fQ1tmLCyaFQO2klDosIqIxiUns6FktW9LpdHjiiScwY8aMwY/9Mjl9+OGHsXTpUgBA\nZGQk1q1bZ61Q7IZuIInVMImVSkyoJwQAZVWtUodCRBKra9ahoaUbU+IDHGLQ3vSkYGzYcQSiCMxM\nDpE6HCKiMUujVqGpnSsdR8Nqv5XVajXeeOMNBAYGnvJYeXk5Ojo6kJqaaq0vb5e6e/qSWBdWYiXj\nqnHCuAA3lNe2w2Q2Sx0OEUkop7+VOM3OW4kH+Hg44/yUECSEew9OYyciItvTOPdVYkVRlDoUu2W1\nbEmlUkGlOv3Lv/fee1ixYsXgf2u1Wtxzzz1oaGjA8uXLcdVVV1krLFnTsZ1YFmJCvVDd2IXjDZ2I\nCPaUOhwikkhOmRaCAKRGO87Rgpsvmyh1CEREY55GrYQoAr0GM5zVPNoxEjbPlnp7e5GZmYnVq1cD\nALy9vXHvvffiqquuQkdHB5YuXYrp06eftoL7SwEBHjaI1rZUFc0AgEB/d4f8/uzF5IlB2JFTg7pW\nPaamSPv3wOuAxgI5XudtnXocqW7DxAhfRE1wnCSWpCPH65zI0nidD42XhwYA4OahgY+nRuJo7JPN\nk9gDBw6c1Ebs7u6OJUuWAAB8fX2RnJyM8vLycyaxjY0dVo1TCvWNnQAAk8HokN+fvQj07JsOnVNc\nj+kJAZLFERDgweuAHJ5cr/PdebUwi0BShI8s4yP7ItfrnMiSeJ0PnaK/jbi6tg1GPdc6nsnZborY\nfFJFXl4eEhISBv973759eOqppwD0DYMqKipCZGSkrcOShYF2Yp6JlVagtws8XZ1QWtUmdShEJJGc\nMsc6D0tERPKhUfe91+eE4pGzWraUn5+PZ555BtXV1VCpVNi6dStefvllNDY2Ijw8fPB5GRkZ2LRp\nE5YtWwaTyYRVq1YhKCjIWmHJGs/EyoMgCIgJ80ZWSSOa23vgyzYPojHFYDQhv7wZQT4uCPZ1lToc\nIiJyMJr+c7A9vUaJI7FfVsuWkpOTT7s259FHHz05AJUKTz/9tLXCsCuD04m5YkdyMaFeyCppRGlV\nG6YlMoklGkuKjrVCbzAhLdYfgiBIHQ4RETkYjXNfEtvNSuyI2f/iOwfCSqx8xPSvnyhjSzHRmMNW\nYiIisqYT7cSsxI4Uk1gZ6R48E8tR21KbEOQBlVKBsmomsURjiSiKyCnVwk2jGryZRUREZEkn2olZ\niR0pJrEyotMb4axWQqngX4vUnFQKRIR44HhDJ++SEY0hx+o70dKhR2q0H38WExGRVQwmsXomsSPF\n39AyousxspVYRmJDvWAWRVTUtEsdChHZyEAr8SS2EhMRkZWwnXj0mMTKSLeeSaycxIT2tRKWsqWY\naMzILG6ESikgJcpP6lCIiMhBsZ149JjEyoQoiujWm7gjVkaiOdyJaEypb9GhqrETiRG+/FlMRERW\nwxU7o8ckVib0BhPMoghXrteRDU9XNYJ8XHCkpg1mUZQ6HCKysqziRgDAlPgAiSMhIiJHdqKdmJXY\nkWISKxO6Hq7XkaOYMC90602oaeySOhQisrKDxY1QCALSY5nEEhGR9bCdePSYxMrEifU6TGLlJDbM\nGwDPxRI5uub2HlTUtiNhgjfcXZykDoeIiByY8+B0YrYTjxSTWJnQ9V/EbCeWl+hQnoslGgsyB1uJ\nAyWOhIiIHJ1CEOCsVrISOwpMYmWClVh5CvFzhZtGhbLqVqlDISIryixugABgcixX6xARkfW5MIkd\nFSaxMsEzsfKkEAREh3qhsbUHbZ16qcMhIito69SjtKoNsWFe8HJ3ljocIiIaAzRqFacTjwKTWJlg\nJVa+BvbFlvFcLJFDyirVQgRbiYmIyHY0rMSOCpNYmeCZWPmK7d8XW8pzsUQOKbO4AQBX6xARke1o\n1Er0Gs0wmc1Sh2KXmMTKxEA7MSux8hMR4gmlQsARVmKJHE5ntwFFR1sRGeIJX0+N1OEQEdEYMbAr\nVs9q7IgwiZWJgXZinomVH2cnJcKD3FFZ14FeA3/QEDmS7NJGmEURGazCEhGRDWmcuSt2NJjEyoSO\nZ2JlLSbUGyaziMq6DqlDISILOrFah0ksERHZzkAltptJ7IgwiZUJnomVt5gwDncicjTdeiMKK5sx\nPtAdgT6uUodDRERjiEbdX4nVc0LxSDCJlYnuHiOUCgFqFf9K5GhwQjGHOxE5jNwyLYwmkVVYIiKy\nucEklpXYEWHGJBM6vREuzioIgiB1KHQaPh7O8PPUoKy6DaIoSh0OEVnAiVZirtYhIiLbGmgn5q7Y\nkWESKxM6vZFDnWQuNswLnd0G1DXrpA6FiEZJ32tCXnkTQvxcEervJnU4REQ0xrASOzpMYmWiW2+E\nC8/DytrguVi2FBPZvbzyJvQazWwlJiIiSTCJHR0msTJgNJnRazCzEitzg+diOdyJyO5llvS3Esex\nlZiIiGyP7cSjwyRWBrgj1j6EBbjDWa1kEktk5wxGM3LLtPD30iA8yF3qcIiIaAxiJXZ0mMTKwOCO\nWLYTy5pCISB6nCdqm3To7DZIHQ4RjVBBZTN6ek3IiA/kMD0iIpLEiRU7TGJHgkmsDLASaz/YUkxk\n/zKLGwCA52GJiEgyLs5sJx4NJrEyoOthEmsvYsO8AXC4E5G9MprMyCnVwsfDGZHjPKUOh4iIxii2\nE48Ok1gZGKjEujCJlb2ocZ4QBFZiiexV8bFWdPUYMTkuAAq2EhMRkUQ42Gl0mMTKwGAllmdiZc/F\nWYWwAHdU1LbDaDJLHQ4RDdNAK3EGW4mJiEhCTioFlAqBldgRYhIrAzpWYu1KTKgXDEYzjtZ3SB0K\nEQ2D2Swiq6QRnq5Og0cDiIiIpKJRK5nEjhCTWBngYCf7EhPWN9zpCM/FEtmV0qpWtOsMSI8LgELB\nVmIiIpKWRq1iO/EIMYmVgYF2YlZi7UNs/4TiUp6LJbIrmcWNADiVmIiI5EHjrEQ3V+yMCJNYGRis\nxPJMrF3w89LAy12Nsqo2iKIodThENARmUURmSSPcNCokhPtIHQ4REdFgOzHfTw4fk1gZ4JlY+yII\nAmJDvdDW1QttW4/U4RDREFTUtqOlQ4+0GH+olPzVR0RE0tOoVTCLIgxGDgsdLv4ml4ETK3aUEkdC\nQxXT31LMfbFE9uFEK3GgxJEQERH14a7YkWMSKwO6HiOc1UooFfzrsBcx/ZNNuS+WSP5EUURmcQOc\n1UokRbKVmIiI5OFEEsvhTsPFrEkGdHojJxPbmfAgd6hVCpSyEkske8cbOtHY2oNJ0X5wUrHjhYiI\n5EGj7nv/z0rs8DGJlYFuvZFDneyMSqlARIgnqhs7B6dLE5E8HexvJc5gKzEREckI24lHjkmsxERR\nhE5v5FAnOxQb5gURQHktq7FEcpZZ3AC1SoGUKD+pQyEiIhrEduKRYxIrsb6x2mA7sR2K5nAnItmr\n0XahtkmH5Cg/OKvZSkxERPIxUMRiJXb4mMRKbHBHLJNYuzM4oZjDnYhkK7O4AQAwJT5A4kiIiIhO\nxnbikWMSK7HBHbE8E2t33F2cEOLniiM17TCZud+LSI4yixuhVAiYFO0vdShEREQnGRzspGc78XAx\niZUYK7H2LSbUC/peE6oauqQOhYh+paG1G8caOpEU6cvheUREJDusxI6cVZPYkpISLFiwAO+//z4A\n4KGHHsKVV16JlStXYuXKlfjxxx8BAJs3b8aSJUuwdOlSfPrpp9YMSXYGJtsyibVPMWFsKSaSq8FW\n4ji2EhMRkfxwxc7IWS1z0ul0eOKJJzBjxoyTPv6nP/0Jc+fOPel5r7zyCjZs2AAnJyf85je/wcKF\nC+Ht7W2t0GRloBLL6cT26ZfnYudPCZM4GiL6pcziRigEAelMYomISIY4nXjkrFaJVavVeOONNxAY\nePa9fLm5uUhJSYGHhwc0Gg0mT56MrKwsa4UlOwNnYtnqZp+CfV3h7uKEsqpWqUMhol9obu9BeU07\n4sO94e7iJHU4REREpxhIYrtZiR02q2VOKpUKKtWpL//+++9j7dq18PPzw6OPPgqtVgtfX9/Bx319\nfdHY2HjO1w8I8LBovFIRlH0Xb3Cgh8N8T2NNUpQf9hfUQXBSwd/bxaKvzWuCxgJrXOd7i/paiedk\njOe/I5IFXoc0FvA6Hx5Xdw0AwAz+2Q2XTct/V199Nby9vTFx4kS8/vrrWLNmDdLT0096jiiKQ3qt\nxsYOa4Roc9rmvoFAhh6Dw3xPY834ADfsB7D/UDXOmxhksdcNCPDgNUEOz1rX+Y7MKggAYkP474ik\nx5/nNBbwOh8+s7kv72nv0PPP7jTOltjbdDrxjBkzMHHiRADAvHnzUFJSgsDAQGi12sHnNDQ0nLMF\n2ZHoeCbW7nFfLJG8tHX1ovR4K6LDvODt7ix1OERERKelUAhQOyk42GkEbJrE3n333Th+/DgAYP/+\n/YiNjcWkSZOQl5eH9vZ2dHV1ISsrCxkZGbYMS1LdPBNr9yKCPaBUCCirYhJLJAfZJY0QAWRwoBMR\nEcmcRq3iYKcRsFrmlJ+fj2eeeQbV1dVQqVTYunUrVqxYgfvuuw8uLi5wdXXFU089BY1Gg/vvvx+3\n3norBEHAnXfeCQ+PsdMTPrBih5VY+6V2UiIi2AMVtR3Q95rg3H9In4ikMbBaZ3I8k1giIpI3jVrJ\nSuwIWC1zSk5Oxrp16075+MUXX3zKxy655BJccskl1gpF1rr1RigVAtQqmxbFycJiwrxwpKYdFbXt\nSJjgI3U4RGNWZ7cBRcdaERHsAX8vyw5aIyIisjSNWom2zl6pw7A7zJwkptMb4apRQRAEqUOhURg4\nF1vKc7FEksop1cJkFjGFVVgiIrIDGrUKeoMJ5iEOt6U+TGIlptMb2UrsAAaHO/FcLJGkBlqJM+LH\nzoBAIiKyXwO7YvVsKR4WJrES6+4xwpVJrN3zcndGgLcGR6rbeCeNSCLdeiMKKpsRFuCGIF9XqcMh\nIiI6p4FiFs/FDg+TWAkZTWb0Gs2sxDqImFBv6PRG1Gq7pA6FaEzKPaKF0SRiCquwRERkJwYqsZxQ\nPDxMYiWk43odhxI3vq+lOK+8WeJIiMamzOJGAOB5WCIishsnklhWYoeDSayEBnbEshLrGCbHBUCp\nELAnv07qUIjGHL3BhLzyJgT5uiLU303qcIiIiIZEo+5vJ9azEjscTGIlNLAjlmdiHYOHqxqp0X6o\nauzEsfoOqcMhGlPyy5vQazAjIz6A096JiMhusBI7MkxiJTRQiWUS6zhmpYQAAKuxRDbGVmIiIrJH\nTGJHhkmshAYqsS48E+swUqP94O7ihH0FdTCazFKHQzQmGIxm5B7Rwt9LgwlBHlKHQ0RENGQD7cTd\nHOw0LExiJaRjJdbhqJQKTJsYhHadAfkVHPBEZAvfHTyObr0JGQmBbCUmIiK7wkrsyDCJlRDbiR3T\nzJRgAGwpJrKF2qYubNpZAU83NS6bPkHqcIiIiIaFK3ZGhkmshAbbiZnEOpSIYA+M83dDTmkjunoM\nUodD5LDMooh3vimC0WTGioVxcHdxkjokIiKiYTkxnZiV2OFgEiuhbu6JdUiCIGBmcjCMJhE/H26Q\nOhwih7U9qxqlVW2YEh+AjIRAqcMhIiIaNo0z24lHgkmshHTcE+uwZiQFQxCAPfm1UodC5JC0rd3Y\n8OMRuGlUWLEwTupwiEOUFv8AACAASURBVIiIRmSwEst24mFhEishVmIdl4+HMxIjfHGkuh11zTqp\nwyFyKKIo4t1vi6A3mHD9glh4uTtLHRIREdGIcLDTyDCJldDgmVg1k1hHNCt5YMATq7FElrQrrxYF\nlS1IifLDjKRgqcMhIiIaMbVKAUFgEjtcTGIl1K03QqNWQqHgSghHlB4XAI1aib35dTCLotThEDmE\n1k49Pv6hDBq1Er+9JJ4rdYiIyK4JggAXtYrtxMPEJFZCOr2RrcQOzNlJiYyEQDS161F8rFXqcIjs\nniiKWLe1GDq9EUvnxsDXUyN1SERERKOmcVayEjtMTGIl1K03cqiTgxtsKc5jSzHRaB0oakB2qRbx\n471xYdo4qcMhIiKyCI1axSR2mJjESkQUxb5KLJNYhxY73hv+XhocLG5kmwjRKHToevHBdyVQqxS4\n6bIEKNhGTEREDkKjVvJ94jAxiZVIT68Josj1Oo5O0b8zVm8wIaukUepwiOzWR9+XokNnwKLZUQjy\ncZU6HCIiIovRqJUwmkQYTWapQ7EbTGIlwvU6Y8fM/pbi3Xl1EkdCZJ9yyrTYV1iPyBBPXDR1vNTh\nEBERWdSJXbFsKR4qJrES0fUnsazEOr5AH1fEhHmh6GgLmtt7pA6HyK7oeoxYt7UYSoWAWy5L4DR3\nIiJyOAO7YgeKXHRuTGIlMrAjlmdix4ZZycEQAewtYDWWaDg+2V6Glg49rpwZgdAAd6nDISIisriB\nJJaV2KFjEiuRwXZiJrFjwtSEIDipFNidVweRO2OJhuRwZTN+yq1BWIAbLpsxQepwiIiIrOJEOzEr\nsUPFJFYig+3EPBM7JrhqVEiP9Uddsw7lte1Sh0Mke/peE9Z+UwRBAG6+bCJUSv66IiIix8RK7PDx\nXYFE2E489sxMDgEA7MlnSzHRuXz+Uzm0bT245LxwRIZ4Sh0OERGR1TCJHT4msRJhO/HYkxTpAy83\nNX4urIfByBHqRGdSVt2G7w8eR5CPC64+P1LqcIiIiKxqsJ2Yg52GjEmsRDideOxRKhSYkRSMrh4j\ncsu0UodDJEsGoxlrtxyGiL42YrWTUuqQiIiIrIqV2OFjEisR7okdmwZ2xrKlmOj0vtxTgdomHeZN\nDkXceG+pwyEiIrI6jfNAEstK7FAxiZUIz8SOTWGB7ggPckdeeRPau3qlDodIVo7Vd2DL3mPw83TG\nkgujpQ6HiIjIJk5MJ2YldqiYxEqkm+3EY9as5BCYzCL2F9ZLHQqRbBhNZry95TDMoojfXprAn41E\nRDRmuLCdeNiYxEpEpzdCpRTgpOJfwVgzLTEISoWA3f/P3p2HR1Wf/eN/n9mz7wlZyEIIawiENaDI\nLogFwSItKKIPXSwutd9Wni5q+9Rej1Xb/vpYoVpqUZG2ClZFBEE2RfY1kIgkIRCykj2TZPaZ8/sj\nyYQlyWSZOWcyeb+ui6vJMJlzZzzQvLnvz+eTUy53KUReY/eJa7h2vQl3jBmE9JQIucshIiKSDM+J\n7TkmKJkYzTb4aVUQBEHuUkhiwQEajBkSgWvXm1BS2SR3OUSyK6tuxsdfXUVIgAbfnZMmdzlERESS\nal8Ty05sdzHEysRgsnE97ADGDZ6IWtgdIjbtugib3YFV84cjQKeWuyQiIiJJcXfinmOIlUlbJ5YG\nprFDIxGgU+FobgXsDp4ZSwPXp18V4nKpHpNGRGP8sCi5yyEiIpKcUqGAWqXgOHEPMEXJwGZ3wGJz\n8HidAUytUmDyyBgcOFuKr6/WYcwQrgEk71arNyGvpB75xQ0ormyCUiFAo1ZCq1ZAq1ZCo1FCq1JC\no1ZAq1FCo1JCq1ZCq2l5jkbV8rFGrYRWpYBGo0Sz0Yp3dl1EoJ8aD84bJve3SEREJBudRgmjmZ3Y\n7nKZohoaGlBZWYm0tDQcOnQI58+fx/LlyxEVxX8x7y0DdyYmANPGDMKBs6U4fKGcIZa8ikMUUV5j\nQH5xvTO41uhNzt8XBEAU3Xe9hxcNR3CAxn0vSERE1M/oNEp2YnvAZYp65plnsHr1aqjVavz+97/H\nypUr8atf/Qp/+9vfpKjPJxl5RiwBGBIbjEHh/jibX92yRtoDnfmcKzXYtPMbNJusUCsVULX9Uimg\nViqgVgnOx9Sqtt9v2TVbfcNzVUoF1EoBkaF+yBoVww3JJHKhsAZfnCtDWKAWESE6RIXqEBnih4gQ\nHQJ07tsYzmZ3oKii0RlY80vq0Wxq/z/SQD81MtMikZYQirTBIUiKCYJCIcBitcNidcBstTt/tX1u\nueVzs8UOi80Os9Vxw8d2DE8KR9aoGLd8H0RERP2VTqNCo8Eodxn9hsufmo1GI+644w68/vrreOih\nh7BixQrs3btXitp8FjuxBACCIGBa+iD858tCnPzmOmaMi3fr6x84U4Itn+dDoRAQHxUAm90Bm80B\nm90Bo9kGfevHNpsDPWmqhQZqMTIpzK210u3Ka5qx4aMcmDvZ5EGnUSIypD3URjp/+SEyVAf/LnY/\nN5ptKCzTI6+4Hvkl9Sgs08Nia1+bHRmiQ0ZqBNIGh2JYQigGRfhD0cFr6TQq6PrYQI2KCkJVVWPf\nXoSIiKif02mUMFvsEEWRzYJu6FaIra2txe7du7FhwwaIooiGhgYpavNZbSGWa2JpWvogfPhlIY7k\nVLgtxDocIv69Px97T5UgyF+NJ7+dgaHxIZ0+XxRF2B1iS6C1i7DaHLDeEHjbPq6oNeDtzy7hwJkS\nhlgPM1lsWP9hS4B9dOEIJEYHobrBiOoGE6obTKhpMKG6wYiqBhNKqpo7fA0/rRIRwX7OcBserENt\nown5JQ0ovt4ER+s8sAAgPioQaYNDMCwhFGkJIQgP1kn43RIREZFOo4IIwGy1O8+Npc65fIcWLVqE\nu+++Gw888ABiY2Px2muvYcqUKVLU5rPaxonZiaXwYB1GJIXhYlEdKusMiA7z79PrGc02/G17LrIv\n1yAuMgA/XpaBqFC/Lr9GEASolC1jxV0ZNjgU+8+U4kxeNeoazQgL0vapVuqYKIp4+7NLKKtuxtwJ\nCZieEQcASBoU1OFzm002Z6itbjChur71Y70JVQ1GlFTdfBaxSilgSHywM7AOTQjhsTZEREQyu/GY\nHYZY11y+Q6tXr8bq1atv+jwo6PYfpjqSl5eHtWvX4pFHHsFDDz2E8vJy/OIXv4DNZoNKpcIrr7yC\nqKgojB49GuPHj3d+3VtvvQWlUtmLb6d/cHZiGWIJLd3Yi0V1OJJTgSXTh/T6dWr1Jvx563mUVDVh\ndEo4fnRfulu7/YIgYNb4eLzz2SV8mV2G++5McdtrU7v9Z0px/OvrSI0PxvLZQ7t8riAICPRTI9BP\n3WXIrW4woqbBhEA/NYbEBUOt8t2/X4mIiPojnhXbMy5/wj127Bg2b96MhoYGiDdsR7lly5Yuv85g\nMOCFF17A1KlTnY/9+c9/xvLly7Fw4UJs2bIFmzZtwrp16xAYGIjNmzf34dvoXwzc2IluMGF4FN7d\nk4cjORVYfGdKh2sPXblSrser286jodmCWePjsXJuGpQK9x8DnTUqBlsPFOCLc6W4d2qSy+4t9UxB\naQP+vS8fQf5q/Oi+9D6/vzeG3ORBwW6qkoiIiNytrfvKHYq7x2WK+vWvf40f/ehHiIuL69ELazQa\nbNy4ERs3brzptbTalhHEsLAw5Obm9rBc32Dkmli6gU6jwsThUTicU4H84noMT+zZetNT31Ti7zu+\nhtXuwIq5aZg7IcFjGwLoNCpMS4/FvtMlOJdfjYkjoj1ynYFIb7Dgrx/lwCGK+OHi0VyXSkRENIA4\nO7E8K7ZbXKaohIQELFmypOcvrFJBpbr55f39W9b72e12/POf/8Tjjz8OALBYLPjpT3+K0tJSzJ8/\nH48++miPr9efcHdiutW09EE4nFOBwzkV3Q6xoihi57EifPBFIbQaJZ5akoGxQyM9XCkwe3w89p0u\nwf4zJQyxbuJwiHjj41zUNZrx7RlDMCo5XO6SiIiISEJtuYDjxN3jMkVNnz4d7733HiZPnnxTKB08\neHCvLmi327Fu3TpkZWU5R43XrVuHxYsXQxAEPPTQQ5g4cSLGjBnT5etERXVvXa43Elu7ZAlxoYgK\n79tGPuQbIiIC8dbuSzh9qQo/XjHeOVLS2X1utTnw2tZz2H+qGJGhfnh+zRSkxHW+A7E7RUUFIWNo\nJM4XVMPkAAbH9N8/i95i866LuFhUh8mjBuHhb6VDoRhYW+v357/PibqL9zkNBLzPey8yIgAAoNap\n+T52g8sQ+8477wAA3njjDedjgiBg3759vbrgL37xCyQlJeGJJ55wPrZixQrnx1lZWcjLy3MZYvvz\nuYK19S0HGZuaTaiy819bqMWUkdHYcaQInx+5gqzRgzo9P7PJaMVr/7mAvOJ6pMQG4clvZyBQrZD0\nz8Sd6YNwvqAaH+zLw4Pzhkl2XV90rqAa7+/NQ1SoDqvuTkNNTZPrL/IhPCeWBgLe5zQQ8D7vG5vZ\nCgCoqm7i+9iqqzDvMsT+61//QkxMjFsK2b59O9RqNZ566innY4WFhVi/fj3+8Ic/wG6348yZM1iw\nYIFbruet2tbEcvtsutHU0YOw40gRDudUIGv0oA6fU1FrwJ+3ZqOyzoiJw6Ow5lujoFVLv9PsuLRI\nhAZqcCSnHN+eMYT3ci9V1hvx90++hlqlwONLx/CoGyIiogGqfWMnNri6w+VPns8884yzG9sTOTk5\neOmll1BaWgqVSoXdu3ejpqYGWq0Wq1atAgCkpqbiN7/5DQYNGoRly5ZBoVBg9uzZyMjI6Pl30o8Y\nzDb4aZUDbmSQuhYbEYDUuGB8fbUWdY3m2/716WJRHTZ8eAHNJhvunZqEpXcN6dVOxu6gUiowY1w8\nPv7qCo59fR0zx8XLUkd/ZrHaseHDCzCYbXh04QgkciybiIhowGo/Yoe7E3eHyxCbnJyMdevWITMz\nE2p1e5dg2bJlXX5denp6t4/NeeaZZ7r1PF9hNNu4qRN1aNqYWFwu0+NYbgWGDWnfpOlQdhne2X0J\nAPBfC0fizoxYuUp0umtsHD45fBX7T5dixtg4j+2I7Ku2fJ6Ha9ebcNfYWEzP6Nnu70RERORbdNqW\nEGvk7sTd4vIQQqvVCqVSifPnz+P06dPOX9R7BpONZ8RShyaPjIZKKeBwTgVEUYRDFLH1QAE27foG\nOo0SP/vuOK8IsAAQFqTF+GGRKKlqwuVSvdzl9CtfZpfh0PlyJMUEcU0xERER8ZzYHnKZpF588UUp\n6hgwHKIIo8UGP22A3KWQFwrQqTFuaCROXapCbmENtu7Nw5m8KsSE++PpZRmI8bLdrGeNT8CpS1XY\nf7YEQxOk2R25vyuqaMS7e/Lgr1Vh7dJ0qFXSr2kmIiIi79I+TsxObHe4DLEzZszocEzw4MGDnqjH\n55ktdogi2ImlTk0bE4tTl6rw3BtHYbM7MCIxFGuXjkGgn/dt+jMiMRSxEf449U0lvjs7DcEBGrlL\n8mrNJivWf3gBNrsDjy9NR1Son9wlERERkRdgiO0Zl0nqn//8p/Njq9WKo0ePwmQyebQoX9a2M7Gf\njiGWOpaeEo5gfzX0BivuzIjFw/OHQ6V0OfkvC0EQMHt8ArZ8nodD58tw79RkuUvyWg5RxN8/+RrV\nDSYsmpaMsUMjXX8RERERDQhatRICOE7cXS6TVHz8zbuOJicnY82aNXj00Uc9VpQvM5habkx2Yqkz\nKqUCT9yfAZsgYHhckNdvmDR19CBsO3gZB8+W4p4pSdx1uxM7jxYh+3INRieH4b47U+Quh4iIiLyI\nIAjQapTsxHaTyyR19OjRmz6vqKjAtWvXPFaQrzO0dWIZYqkLQxNC+s2h4f46FaaOjsHBc2U4f7kG\n49LYYbxV7tVafHioEOHBWvxg8WgGfSIiIrqNTqNkJ7abXCapDRs2OD8WBAGBgYH4n//5H48W5cva\nQqw/x4nJh8zMjMfBc2XYf7akX4ZYfbMFm3ZeRGl1M0YmhWHMkAiMSg53y5/TWr0Jb3ycC4Ug4EdL\n0hHkz3XDREREdDudRoVmk1XuMvoFlz+hPf7448jKyrrpsb1793qsIF9nNLETS74nMSYIQxNCkFNY\ni8o6A6LDvGsX5a4Ulumx/sMLqGs0Q6NW4ND5chw6Xw6lQkBqfAjGDAnHmCERGBwd2OPRbpvdgQ0f\n5aDJaMVDdw9Dahx3cCYiIqKO6TRK1Oi591B3dJqkSkpKUFxcjJdeegk///nPIYoiAMBms+F///d/\nMXfuXMmK9CXOTixDLPmY2ZnxKChpwMGzZVg+e6jc5XTLl9lleHfPJdgdIr49YwgWTEnE1YpGXLhc\ngwuFtcgvrkdecT0++KIQoYEapA+JQEYPurTv7StAYZkeWaNjMCsz3uXziYiIaODSaZSw2hywOxxQ\nKrxzU09v0elPYVVVVdi5cydKS0uxfv165+MKhQLf/e53JSnOF3GcmHzVhOHRCNqXj0Pny7Bkego0\nau89/9Rqc+Bfe/Nw8FwZAnQq/PC+0UhPiQAApMaFIDUuBEumD4HeYEHulVpcKKxBTmEtvjpfjq/O\nl0MhCBgaH4wxqRGddmmP5VZg35kSxEcGYPX8EV6/QRcRERHJq21S02SxI0DHENuVTpNUZmYmMjMz\nMWPGDHZd3cjIjZ3IR6lVCtw1Ng6fHi3CiYuVuDMjVu6SOlTXaMaGDy/gcpkeg6MD8cT9Yzo9rzXY\nX4Opowdh6uhBcIgirpY34kJhDS4U1iC/pAF5JQ344ItChARqMCYlAmNSIzA6OQx1jWa89dk30GmU\nWLs0HVqN9wZ6IiIi8g7Os2LNdgTo1DJX491cJqkRI0bgqaeeQl1dHTZv3oytW7di0qRJSE5OlqA8\n38MjdsiXzRgXh53HinDgbIlXhti84nps+CgH+mYLskbHYPWCEdB2s2OsEAQMiQvGkLhg3HdnCpqM\nVuRcqcGFy7XIuVKDry6U46sLLV1arUYJi9WBtUvSERsR4OHvioiIiHyBTtPWieUOxa64TFLPP/88\nHnzwQWzatAlAyzmxzz33HDZv3uzx4nyRkWtiyYdFhvhhbGokzhVU40q5HimxwXKXBAAQRRH7Tpfg\nvf0FEEVgxZw0zJ2Y0KcR30A/NbJGDULWqJYubVFFe5e2sEyPhVlJmDgi2o3fBREREfkyZyeWZ8W6\n5DJJWa1WzJkzB2+99RYAYNKkSZ6uyadxTSz5utnj43GuoBoHzpQi5V75Q6zFasfbn13C0dwKBPur\n8aMl6RieGObWaygEASmxwUiJDcbiO1JgszugUnItCxEREXUfQ2z3deunLL1e7+xY5Ofnw2w2e7Qo\nX2Y026BSClCruEaOfNOolHBEh/rh+MXraDLKe9ZZdb0R//vuaRzNrUBKbDCef2SS2wNsRxhgiYiI\nqKfaxonbJjepc906J3b58uWoqqrCokWLUFdXh1deeUWK2nySwWTjKDH5NIUgYGZmPN4/UIDDF8ox\nf3KiLHXkXq3FGx/nosloxV1jY/HgvOFQqxguiYiIyDuxE9t9LtPUlClT8NFHHyEvLw8ajQYpKSnQ\narVS1OaTjGYbdyYmn3dnRiw+PFSIA2dLMW/SYCgkPF5GFEV8dvwatn1xGUqFgNULhmPGOJ7RSkRE\nRN5Np+XGTt3lsi3x8MMPQ6fTISMjAyNGjGCA7SOD2cb1sOTzAv3UmDwyGpV1Rnx9tVay65osNvz1\n41xsPXgZoYFa/PfK8QywRERE1C+wE9t9LtPUyJEj8X//93/IzMyEWt1+XtHUqVM9WpgvstocsNoc\n7MTSgDB7fAIOX6jA/tOlSE+J8Pj1rtca8Jf/XEBZdTOGJYTgR0vHICRA4/HrEhEREbkDQ2z3uUxT\nFy9eBACcOnXK+ZggCAyxvcDjdWggSYkNRvKgIGRfrkZ1gxGRIX4eu9a5gmps/CQXRrMdcyckYPns\nodxciYiIiPoVnhPbfS7TVFfnwW7cuBHf//733VqQL2sLsezE0kAxa3w8Nu38Bl+cK8O3Z6S6/fUd\noojtX13B9sNXoVYp8P1vjcLU9EFuvw4RERGRp7ET2319alUcOnTIXXUMCDwjlgaaySNjEKBT4VB2\nGaw2h1tfu6SyCS9uPo3th68iMkSHXz40gQGWiIiI+i2G2O7rU5oSRdFddQwIBhM7sTSwaNVK3JkR\ni90ninE6rxJZo/oeMs1WO7YfvoI9J4phd4iYNCIaq+YPR6Cf2vUXExEREXkpjhN3X5/SlCDhsRm+\ngGtiaSCamRmP3SeKceBMaZ9DbE5hDd7ZfQnVDSZEBOuwav5wZKR6ftMoIiIiIk9TqxRQKQV2YruB\naUpCBq6JpQEoJswf6SnhyLlSi+LKJgyODuzxazQ0mfHv/QU4/vV1KAQB90xJxOI7UqBtHbshIiIi\n8gU6jYohthuYpiTUNk7MNbE00MwaH4+cK7U4cLYUD88f3u2vc4givswuw7YDl2Ew2zAkLhgPzx+O\nxJggD1ZLREREJA+dRslx4m7oU5pKTk52UxkDg4HjxDRAjU2NRESwFkdzKvDAzNRuTSOUVjXh7d2X\nUFDSAD+tEg/dPQwzx8VDoeAyBiIiIvJNOo0StXqz3GV4PZe7E5eWluKpp57CqlWrAADvv/8+rl69\nCgD47W9/69HifA2P2KGBSqEQMGNcPMxWO47kVHT5XIvVjg++uIzfbDqJgpIGTBwehd99Lwuzxycw\nwBIREZFPaxsn5ga6XXMZYp977jncd999zjcyJSUFzz33nMcL80UcJ6aBbPrYOCgVAvafKen0L+bc\nq7V4/s0T+PRoEUIDNXhqWQbWLh2DsCCtxNUSERERSU+nUcIhirC4+WhCX+MyxFqtVsyZM8e5E/Gk\nSZM8XpSv4u7ENJCFBGgwcUQ0ymsMuHSt/qbf0zdbsPGTXPzx3+dQ1WDE/MmD8cL3pmDc0EiZqiUi\nIiKSHs+K7Z5upSm9Xu8Msfn5+TCbOafdGwazDQIAHUMsDVCzMuNx/Ovr2H+2FCOSwiCKIg6dL8fW\nAwVoNtmQPCgIqxeMQNIgbtxEREREA8+NZ8WGBGhkrsZ7uUxTjz/+OJYvX46qqiosWrQIdXV1eOWV\nV6SozecYzTbotEooeL4uDVBpCSFIiArE2bwqXLxai48PX0VecT20GiVWzk3julciIiIa0JydWDM7\nsV1xGWKzsrLw0UcfIS8vDxqNBikpKdBquT6tNwwmG0eJaUATBAGzx8fjnd2X8Mq/zwEAxg+Lwsq5\naQgP1slcHREREZG8dNq2cWIes9OVThPVa6+91uUXPvHEE24vxtcZzTaEB/MfAGhgyxodg48PX4FC\nEPDQvGHIHBYld0lEREREXqF9nJid2K50GmJttpb0X1RUhKKiIkycOBEOhwMnTpzAqFGjJCvQVzhE\nEUazDf7aALlLIZKVTqPC/34/C2qVAiqly73liIiIiAYMbuzUPZ2G2KeffhoA8Nhjj2Hr1q1QKlve\nUKvVip/85CfSVOdDzBY7RPCMWCKAfw6IiIiIOtIeYjlO3BWXbZDy8vKbznQUBAFlZWUeLcoX8YxY\nIiIiIiLqCseJu8dlopo5cybmz5+P0aNHQxAEXLx4EXPmzJGiNp9iaD0jlh0oIiIiIiLqCMeJu8dl\novrJT36CpUuXIi8vD6Io4sknn8TQoUOlqM2nGM3sxBIRERERUefaGl4cJ+6ay0Rlt9tx7tw55OTk\nAGhZE8sQ23Nt48TsxBIRERERUUfYie0el4nqhRdeQG1tLaZMmQJRFLFr1y6cO3cOzz77rBT1+Qxn\nJ5YhloiIiIiIOsA1sd3jMlEVFBTg3XffdX7+0EMPYeXKlR4tyhcZnOPEapkrISIiIiIib+TsxJo5\nTtwVl7sTW61WOBwO5+d2ux12O/9loKfaN3ZSylwJERERERF5I21riDWyE9sll53YGTNmYNmyZZg0\naRIA4Pjx41i4cGG3XjwvLw9r167FI488goceegjl5eVYt24d7HY7oqKi8Morr0Cj0WD79u14++23\noVAosHz5cjzwwAN9+668kLHtiB0tO7FERERERHQ7hSBAq1ZyYycXXIbYtWvXYtq0acjOzoYgCPjt\nb3+LjIwMly9sMBjwwgsvYOrUqc7HXn31VaxcuRL33HMP/vSnP2Hbtm1YsmQJ1q9fj23btkGtVmPZ\nsmWYN28eQkND+/adeRl2YomIiIiIyBWdRsk1sS64HCduaGhAQEAAVq9ejeTkZBw6dAhVVVUuX1ij\n0WDjxo2Ijo52Pnb8+HHnGbOzZs3C0aNHkZ2djTFjxiAoKAg6nQ7jx4/HmTNn+vAteSeuiSUiIiIi\nIlcYYl1zGWKfeeYZVFZW4urVq3j55ZcRGhqKX/3qVy5fWKVSQafT3fSY0WiERqMBAERERKCqqgrV\n1dUIDw93Pic8PLxbIbm/ad+dmJ1YIiIiIiLqmE6j4jixCy7HiY1GI+644w68/vrrePDBB7FixQrs\n3bu3zxcWRbFHj98qKiqozzVIyWp3QK1SIC7Wt8akybP6231O1Bu8z2kg4H1OAwHvc/cICtSg6Hoj\nwiMCoVQIcpfjlboVYmtra7F7925s2LABoiiioaGhVxfz9/eHyWSCTqfD9evXER0djejoaFRXVzuf\nU1lZiXHjxrl8raqqxl7VIBd9kwV+GmW/q5vkExUVxPuFfB7vcxoIeJ/TQMD73H3a5jZLSuvhr3MZ\n13xWV/8o4nKceNGiRbj77ruRlZWF2NhYrF+/HlOmTOlVIdOmTcPu3bsBAHv27MH06dMxduxYXLhw\nAXq9Hs3NzThz5gwmTpzYq9f3ZgazDX5cD0tERERERF3QaVuCK0eKO+cy2q9evRqrV6++6fOgINej\nAjk5OXjppZdQWloKlUqF3bt34w9/+AN+/vOf47333kNcXByWLFkCtVqNn/70p1izZg0EQcDjjz/e\nrdfvb4xmGyKCtXKXQUREREREXkzXelYsN3fqXKch9ne/+x2effZZrFy5EoJw+yz2li1bunzh9PR0\nbN68+bbHN23ajSToRAAAIABJREFUdNtjCxYswIIFC7pTb79ktTlgtTngrx244wBEREREROQaQ6xr\nnaaqZcuWAQCefvppyYrxVUbnGbEMsURERERE1Dk/DceJXel0TeyIESMAABMmTEBzczOys7Nx/vx5\nmM1mTJo0SbICfUH7GbEMsURERERE1Dl2Yl1zubHTL3/5S7z55pvQ6/Wor6/HX//6Vzz33HNS1OYz\nDCZ2YomIiIiIyDVu7OSay1R1+fJlbNu2zfm5KIpYvny5R4vyNW3jxFwTS0REREREXWEn1jWXndiY\nmBiYzWbn5xaLBYMHD/ZoUb6mfZyYR+wQEREREVHn2kJsWyOMbueyNSiKIubOnYvx48dDFEVkZ2cj\nLS0N69atAwC8/PLLHi+yv2vf2Enp4plERERERDSQ6ZwbO7ET2xmXIXbevHmYN2+e8/NZs2Z5tCBf\n1LYm1l/LTiwREREREXWO48SuuQyxS5cuRV5eHq5du4a5c+dCr9cjODhYitp8hoGdWCIiIiIi6ob2\nEMtx4s64DLFvvfUWduzYAYvFgrlz52LDhg0IDg7G2rVrpajPJxhNXBNLRERERESucZzYNZcbO+3Y\nsQPvv/8+QkJCAADr1q3DwYMHPV2XT2EnloiIiIiIuoPjxK65DLEBAQFQKNqfplAobvqcXGs/Yoed\nWCIiIiIi6pxapYBCEDhO3AWX48SJiYl47bXXoNfrsWfPHuzcuROpqalS1OYzDGYbBAA6dmKJiIiI\niKgLgiBAp1GyE9sFly3V559/Hn5+foiJicH27dsxduxY/PrXv5aiNp9hMNmg06qgEAS5SyEiIiIi\nIi+n0yphMjPEdsZlJ1atVmPNmjVYs2bNbb/305/+FH/84x89UpgvMZpt8GcXloiIiIiIukGnUaGh\nySx3GV6rT4tbKysr3VWHTzOYbfDjelgiIiIiIuoGjhN3rU8hVuB4rEsOUYSJnVgiIiIiIuomP40S\ndocIq80hdyleidsMe5jJbIcInhFLRERERETd035WLHco7ghDrIcZzFYAPCOWiIiIiIi6h2fFdq1P\nIVYURXfV4bOMrbuK8YxYIiIiIiLqjvZOLENsR/oUYhcuXOiuOnyWwdTaidW53AiaiIiIiIgIutYp\nTqOZ48QdcZmsduzYgY0bN0Kv10MURYiiCEEQcPDgQaxYsUKKGvu19k4sQywREREREbnGceKuuUxW\nf/nLX/C73/0OcXFxUtTjc9rWxPqzE0tERERERN3AjZ265jJZJSUlYdKkSVLU4pPaOrF+7MQSERER\nEVE3sBPbNZfJKjMzE3/6058wefJkKJXtO+xOnTrVo4X5irY1sRwnJiIiIiKi7mCI7ZrLZHXkyBEA\nwNmzZ52PCYLAENtN7MQSEREREVFPcJy4ay6T1ebNm6Wow2dxTSwREREREfUEO7Fd6zRZ/e53v8Oz\nzz6LlStXQhCE235/y5YtHi3MVxjYiSUiIiIioh5giO1ap8lq2bJlAICnn376tt/rKNRSx4zONbFK\nF88kIiIiIiLiOLErnYbYESNGAAAmT56M5uZmNDQ0AAAsFgt+9rOfYdu2bdJU2M8ZzHaolAqoVQyx\nRERERETkmq61AWYysxPbEZczrhs3bsQbb7wBi8UCf39/mM1mLFq0SIrafILBbON6WCIiIiIi6rb2\ncWJ2YjuicPWE3bt348iRIxg7diyOHTuGP/zhD0hLS5OiNp9gNNu4HpaIiIiIiLpNqVBAo1JwTWwn\nXIbYgIAAaDQaWK0tazvnzJmDffv2ebwwX2Ew2XhGLBERERER9YhOo2SI7YTLdBUSEoLt27dj2LBh\n+MUvfoHU1FRUVlZKUVu/Z7XZYbM7uKkTERERERH1iE6j4jhxJ1yG2Jdeegk1NTWYN28e3n77bVRU\nVOBPf/qTFLX1e87jdXRqmSshIiIiIqL+RKdRosFgkbsMr+QyxG7evBk/+MEPAACPPfaYxwvyJQbn\n8TocJyYiIiIiou7TaZQwW+xwiCIUPOL0Ji7XxObl5aGoqEiKWnyOsbUTyxBLREREREQ9oWvNEGau\ni72Ny3R16dIlLFy4EKGhoVCr1RBFESaTCcePH5eivn7NYG7pxPrxiB0iIiIiIuqB9mN27Dzt5BYu\n343o6Gi88cYbEEURgiBAFEXcf//9UtTW77ETS0REREREvXHzWbFaeYvxMp2mq+3bt2P9+vUoLy/H\nypUrnY/bbDbExsZKUlx/xzWxRERERETUGzpNS4bgMTu36zRdLV68GPfeey9+9atf4cknn3Q+rlAo\nEB0dLUlx/V1bJ5btfyIiIiIi6okbx4npZl2mK6VSid///vdS1eJz2tbE+nNNLBERERER9UB7J5Zn\nxd7K5e7E1HtGEzuxRERERETUc+zEdo4h1oOcnViGWCIiIiIi6gGG2M5Jmq62bt2K7du3Oz/PyclB\neno6DAYD/P39AQD//d//jfT0dCnL8hiuiSUiIiIiot7gOHHnJE1XDzzwAB544AEAwIkTJ7Br1y4U\nFBTgxRdfxLBhw6QsRRIGkxUCAJ1WKXcpRERERETUj/i1ZgiTmZ3YW8k2Trx+/XqsXbtWrstLwmC2\nQ6dVQSEIcpdCRERERET9CI/Y6Zwsc67nz59HbGwsoqKiAACvvvoq6urqkJqail/+8pfQ6XRylOV2\nRrOV62GJiIiIiKjH2tfEcpz4VrIkrG3btmHp0qUAgIcffhjDhw9HYmIifv3rX2PLli1Ys2aNy9eI\nigrydJl9ZrLYERXm3y9qJe/Ee4cGAt7nNBDwPqeBgPe5e6m0agCAQxD43t5ClhB7/PhxPPvsswCA\nefPmOR+fPXs2du7c2a3XqKpq9Eht7uIQRRhMNmhUCq+vlbxTVFQQ7x3yebzPaSDgfU4DAe9z9zO3\njhE3NJoG5HvbVXCXfE3s9evXERAQAI1GA1EU8cgjj0Cv1wNoCbdpaWlSl+QRJrMNIni8DhERERER\n9ZxGrYAgcE1sRyRPWFVVVQgPDwcACIKA5cuX45FHHoGfnx9iYmLw5JNPSl2SRxjMLbPrPF6HiIiI\niIh6ShAE6DRK7k7cAckTVnp6Ov7+9787P1+4cCEWLlwodRkeZzC1hFh/HUMsERERERH1nE6j4sZO\nHZDtiB1fZ2QnloiIiIiI+kCnUXKcuAMMsR7SNk7MNbFERERERNQbDLEdY4j1kLZOLMeJiYiIiIio\nN3QaFWx2B2x2h9yleBWGWA9xrollJ5aIiIiIiHpBp1EC4A7Ft2KI9RCuiSUiIiIior5oD7Hc3OlG\nDLEeYuA4MRERERER9YFO05Il2Im9GUOsh7ATS0REREREfaHTcpy4IwyxHsI1sURERERE1BftnViO\nE9+IIdZD2IklIiIiIqK+cK6JNbMTeyOGWA8xmG1QqxRQq/gWExERERFRz7WFWCM7sTdhwvIQg9nO\nLiwREREREfWaHzd26hBDrIcYTVauhyUiIiIiol7jObEdY4j1EIPZxuN1iIiIiIio17ixU8cYYj3A\narPDZhc5TkxERERERL3GTmzHGGI9gMfrEBERERFRX3F34o4xxHqAgcfrEBERERFRH+m0HCfuCEOs\nB7SFWK6JJSIiIiKi3uI4cccYYj3AyE4sERERERH1kUqpgEopMMTegiHWA7gmloiIiIiI3EGnUXGc\n+BYMsR7Q1olliCUiIiIior7QaZTsxN6CIdYDnBs7cU0sERERERH1AUPs7RhiPYCdWCIiIiIicged\ntmWcWBRFuUvxGgyxHsA1sURERERE5A46jRKiCFhsDrlL8RoMsR7A3YmJiIiIiMgddJq2s2I5UtyG\nIdYDnJ1YroklIiIiIqI+cJ4Va+YOxW0YYj3AaLZBAKBtveGIiIiIiIh6wxli2Yl1Yoj1AIPZBj+t\nCgpBkLsUIiIiIiLqx9rHidmJbcMQ6wFGs42jxERERERE1Gd+rZ1YIzuxTgyxHtDWiSUiIiIiIuqL\n9nFidmLbMMS6mcMhwmi283gdIiIiIiLqM+5OfDuGWDdr+xcSdmKJiIiIiKiv2ncnZohtwxDrZjxe\np3OiKMJit8hdBhERERFRv8Fx4tsxabmZwTzwOrGiKKLZZoDe3IgGsx4NFj305kbUW/TQm/VosLQ8\nrrfoYXXYsGjIAixIni132UREREREXk+n5TjxrQZO0pKIsTXE+tKa2HpzA0qbKloDqR4N5kboLfrW\nwNoIvVkPm9j5HyqFoECQOhCxAYNQZazBZ1f3ISt2AkK1IRJ+F0RERERE/Q/Pib2d7yQtL+Erndjr\nhipkV+UguyoXV/XXOnyOQlAgRBOM+KA4hGqCEawNRogmCCHaYAS3/m+INhiB6gAohJbJ9SNlJ7Dl\nm234pHA3Vo1cLuW3RERERETU7/Cc2Nv176TlhfrrmlhRFHGtsQTZVbnIrspBhaESQEtQHRY2FMNC\nUxGqbQmqoa0hNUDt7wyn3ZUVOxEHir/C8fLTmJVwJxKC4jzx7RARERER+QR2Ym/Xv5JWP9Cfxont\nDjsK6q8gu7ql41pvbgAAqBVqjI0cjYyo0UiPHIlAdYDbrqkQFFg69F6sz34THxZ8iifGfQ+CILjt\n9YmIiIiIfImWIfY23p+0+hnnOLGXdmItdgsu1uYhuyoXOdUX0WwzAAD8VH6YPGg8xkalY2T4MGiV\nGo/VMCpiOEaGD8PF2jx8XZuH0RHDPXYtIiIiIqL+TCEI0GqUHCe+gXcmrX7MGzuxzVYDcqovIrs6\nF1/XXILVYQUAhGpDcFfMNIyNGo200CFQKpSS1bR06L345kQ+PizYgRFhQyW9NhERERFRf6LTKHlO\n7A28J2n5COeaWJlDrNVhw9GykzhXdQH59YVwiA4AQIx/NMZGjcbYqNFIDEro8ZpWd4kPjEVW7EQc\nLT+JYxWncEfcFFnqICIiIiLydjqNCkaTVe4yvAZDrJsZvWR34qNlJ/Fe3ocAgKTgwRgbORpjo9Ix\nKCBa1rpu9K0hd+P09XPYUbgHE6LHQafSyl0SEREREZHX0WmUqNOb5C7DazDEupm3HLGTV1cAAPj5\npKcx2Et3AA7VhmBO4gzsuroX+4q/xL0p8+QuiYiIiIjI6/hplLDYHLA7HFAq5Jmk9CZ8B9zMaLZB\no1JArZLvrRVFEQX1VxCqDUFCYKxsdXTH3MQZCNIEYm/RQTSY9XKXQ0RERETkddrOijVzh2IAEofY\n48ePIysrC6tWrcKqVavwwgsvoLy8HKtWrcLKlSvx4x//GBaLRcqS3M5gssnehb1uqEKjtQlDQ1O8\n/vganUqLb6XcDYvDih2Fe+Quh4iIiIjI6/Cs2JtJ3i6cPHkyNm/ejM2bN+O5557Dq6++ipUrV+Kf\n//wnkpKSsG3bNqlLciuD2QZ/mY/Xya8vBACkhQ6RtY7umho7CYMCYnC0/CRKm8rlLoeIiIiIyKu0\nhVgjQywALxgnPn78OObMmQMAmDVrFo4ePSpzRb0niiKMZvk7sQWtIXZoPwmxSoUSS1MXQoSIjwp2\nyl0OEREREZFXaRsn5lmxLSQPsQUFBXjsscewYsUKHD58GEajERqNBgAQERGBqqoqqUtyG6vNAZtd\nlPV4nbb1sEHqQMT4R8lWR0+NjhiB4WFD8XXtJVysyZO7HCIiIiIir8Fx4ptJmraSk5PxxBNP4J57\n7kFxcTEefvhh2O3t/yFEUez2a0VFBXmixD5p2/Y6NFgnW33Xm6pQb25AVsJ4REcHy1JDb62ZtBz/\nvedFfHJ1F+4clgkFd17zyvucyN14n9NAwPucBgLe554TER4AANDq1HyfIXGIjYmJwcKFCwEAiYmJ\niIyMxIULF2AymaDT6XD9+nVER3fvHNOqqkZPltor5TXNAAClIF99x8tzAACD/QZ75XvUlQCEYvKg\n8ThecRo7LhzE1LhJcpckq6iooH7335Cop3if00DA+5wGAt7nnmW3towRX69qGjDvc1dhXdJW1/bt\n2/Hmm28CAKqqqlBTU4P7778fu3fvBgDs2bMH06dPl7Ikt/KGM2IL6lo3dQrrH+thb7VoyHyoFSp8\nUrgbZnv/3qmaiIiIiMgdOE58M0lD7OzZs3Hy5EmsXLkSa9euxW9+8xv85Cc/wUcffYSVK1eivr4e\nS5YskbIktzKaWkKsnGtiC+oL4a/yQ2xAjGw19EWYLhRzBt+FBose+699KXc5RERERESy48ZON5M0\nbQUGBuL111+/7fFNmzZJWYbHyN2JrTPVo9pUizGRo6AQ+u960nlJM3G47AT2XDuIaXFTEKLl3D8R\nERERDVzsxN6s/yYdL9QWYuU6J7ag/goAYGhoiizXdxedSod7h8yDxW7Bzit75C6HiIiIiEhWzhBr\nZogFGGLdyihzJza/9XzYtH5yPmxXpsVORox/NA6XnUB583W5yyEiIiIiko1Oy3HiGzHEupFB5jWx\nBfVXoFVqkBAYJ8v13UmpUGLp0IUQIeKjgk/lLoeIiIiISDYcJ74ZQ6wbtXVi5QixeksjrhsqMSQk\nGUqFUvLre0J6xEikhQ5BTs03+KY2X+5yiIiIiIhk4ecMsezEAgyxbiXnmti29bC+MErcRhAE3D/0\nWwCADws+hUN0yFwREREREZH0VEoFlAqBndhWDLFu1HbEjhxrYts3dfKdEAsAicEJmBQzHiVNZThZ\ncVbucoiIiIiIJCcIAnQaJUNsK4ZYNzKYbRAEQKuRfpy3oL4QaoUKScEJkl/b0xanzodKocL2ws9g\nsVvkLoeIiIiISHItIZbjxABDrFsZzTb4a1VQCIKk1222GlDWVIGU4CSoFPJsKuVJ4bowzB48HfXm\nBuwv/krucoiIiIiIJKfTqNiJbcUQ60YGs02WUeLL9VcgQuz358N25e6kmQhUB2BP0X7oLY1yl0NE\nREREJKm2cWJRFOUuRXYMsW5kMNlk2ZnYualTmG+th72Rn8oPC1PmwWy3YOeVvXKXQ0REREQkKZ1G\nCbtDhM3OzU4ZYt3E4RBhsthl6cTm1xdCKSiRHJwo+bWldGfcFET7R+Jw2XFUNFfKXQ4RERERkWR0\nmpacYeRIMUOsuxgt8hyvY7KZUNxYiqTgwdAoNZJeW2pKhRJLUu+FQ3Tgo8s75S6HiIiIiEgyOudZ\nsQyxDLFuItfxOpcbinx+PeyNMiJHYWhoCi5Uf428ustyl0NEREREJAlda84wmblDMUOsmxhabyap\n18QW1BcCANJ87HzYzgiCgPuHfgsA8J+CHXCIXBNARERERL6Pndh2DLFuYjTL04ktqL8CAQKGhCRJ\nel05JQUPxsSYcShuLMXx8tNyl0NERERE5HHtIZadWIZYNzGYpF8Ta7FbUKQvxuCgeOhUOsmu6w2W\npC6ERqHGx5d3wWA1yl0OEREREZFHtW3sxE4sQ6zbGGToxF7VX4NdtA+YUeIbhelCMT95DhqtTdh5\n5XO5yyEiIiIi8iiOE7djiHUTOdbE5te1rIcdKJs63WpO4l2I9IvAF6VHUNZUIXc5REREREQe4+zE\ncmMnhlh3ca6JlXCcuG097EANsWqFCg+kLYZDdGBr3scQRVHukoiIiIiIPEKnZSe2DUOsmzjXxErU\nibU6bLiiL0Jc4CD4q/0luaY3So8cifSIEcirv4wzleflLoeIiIiIyCM4TtyOIdZNjBKPE1/Tl8Dq\nsGHoAFwPe6tvpy2GSlDiPwU7YLZb5C6HiIiIiMjt2jd24jgxQ6ybGCQeJ247H3agjhLfKNo/EnMS\nZ6De3IA9V/fLXQ4RERERkdv5sRPrxBDrJlJ3YvMZYm8yP3k2QrUh2HvtC1QaquUuh4iIiIjIrThO\n3I4h1k0MJhs0KgVUSs+/pXaHHYUNVxHjH41gTZDHr9cfaJUa3D/0XthEOz7I/0TucoiIiIiI3Err\nDLEcJ2aIdROD2SbZKHFJUxnMdgu7sLcYHz0WaaFDkFNzETnVF+Uuh4iIiIjIbZQKBTQqBYzsxDLE\nuovRbJN8lDiNmzrdRBAELB+2BApBgW3522F18F+piIiIiMh36DRKjhODIdYtRFGEwSRdiOWmTp2L\nCxyEu+KnospYg/3XvpS7HCIiIiIit9FpVBwnBkOsW1htDtgdIvwkCLEO0YGC+quI1IUjTBfq8ev1\nR/em3I1AdQA+u7oPdaZ6ucshIiIiInILnZadWIAh1i3ajtfxl2BNbFlTBYw2I8+H7YK/2g/3pS6E\nxWHFhwWfyl0OEREREZFb6DQqmC12OERR7lJkxRDrBm3H60jRiS2ovwKAo8SuZMVOQFLwYJyuzEZe\n3WW5yyEiIiIi6rO2Y3bMA7wbyxDrBgaTdGfEtq2HTQtjJ7YrCkGB7wxbAgECtuZ9DLtjYP9B9zbn\nq3JxqbYA4gD/V0QiIiKinuBZsS2k2YnIx0nViRVFEQX1VxCqDUGELtyj1/IFScGDMTV2Io6Un8Sh\n0mOYOfgOuUsiAIdKj+Hfl/4DABgSkoSFKfMwIiwNgiDIXBkRERGRd9NpWvJGy+ZOWnmLkRE7sW4g\n1ZrY64YqNFqbMDQ0hT/wd9Pi1Hvgp9Jhx5XdaLQ0yV3OgJddlYv3Ln2IQHUAxkSOQmFDEV4793f8\n8fQGXKzJY2eWiIiIqAvsxLZgiHUDg0Sd2Hzn0TocJe6uIE0g7k25G0abCdsv75K7nAHtcv1VbMrd\nArVChbVj/wuPZTyCn0/6McZGjsYVfRFey2aYJSIiIuqKM8SaB/YxOxwndgOjRGtinethualTj9wV\nPxVHyk7gaPkp3BE/BcnBiXKX1CWL3YITFWcgVtmQ6jcUcYGD5C6pz8qbr+P185tgFx14LONRJAUP\nBgAMDorHDzJWo7ixFLuu7EV2dS5ey/47UoKTcG/KPIwI55gxERERUZv2ceKB3YlliHUDKTqxbeth\nA9UBiPGP9th1fJFSocTyYffhz2ffwPuXPsbPJj4OheB9QwhNlmZ8UXoEX5YcQZO12fn4IP9oZEZn\nYHx0BmIDYvpdqKsz1WP9uTdhsBnx8MjvYHTE8NuewzBLRERE5JpOy3FigCHWLRqaLQA8uya2xlSL\nenMDxkWN4Q/zvZAWlooJ0WNxujIbx8pPY1rcJLlLcqo21mJ/8Zc4UnYSVocV/io/LEieg7SYRHxZ\neBJf13yDXVf3YtfVvYjxj8b46DHIjM5AXMAgr78XDFYDNmT/A3XmetyXeg+mxE7o8vntYbYMu67u\nRXZVTmuYTcTClHkYGT7M679nIiIiIk9pXxPLcWLqg0aDBScvViIkUIOYMH+PXSe/rm2UmOthe2vp\n0HtxofprfHx5J8ZFpcNf7SdrPdf0Jdh77QucqTwPESLCdWGYPXg6psZOgk6lRVRUEEYEjITJZkZu\nzUWcqbyA3JpvsOvqPuy6ug8x/lHIjGoJtPGBsV4X7qx2K14//zbKmiswM+EOzEuc2e2vHRwUhx+M\nefimMLs++02GWSIiIhrQOE7cgiG2jz4/VQyz1Y777xoCtcpzI6oF9VcAAEO5HrbXwnShWJA8B9sL\nP8OnV/bggWH3SV6DKIq4WJuHz699gby6AgBAQmAc5iXOQGZ0BpQK5W1fo1NpMSFmHCbEjGsNtN/g\nbOV55NR8g8+K9uOzov2I9otEZnQGMqMzkOAFgdYhOvDW1//C5YYryIzOwLfTFvWqJoZZIiIionZ+\nrZ1YI0Ms9VaT0Yq9p0oQHKDBjHFxHr1WQX0h/FR+PrHJj5xmJ96Fo+Un8WXpUdwRN0Wy99PusON0\nZTb2XvsCpU3lAIARYWmYmzSjR2ektgTasZgQMxZmuwW5Nd/gTOV55FZfxO6i/dhdtB9RfhHONbQJ\ngXGShzxRFLE172Ocq8pBWugQrB75nT6vQW4LsyWtYfZca5hNDk7EguTZSAlJQoDKn4GWiIiIfNrN\n58QOXAyxfbD3VDFMFjvuuzMFGvXtHTR3qTPVo9pUizGRI71yQ6L+RK1QYVnaYvz1/Ca8n/cRfpz5\nQ48GH5PNjCPlJ7D/2iHUmeuhEBSYGDMOcxNnYHBQfJ9eW6vUYHxrWG0LtGcrzyOn+iL2FB3AnqID\nzkA7PT4L4bowN31XXdtdtB9flh5FfGAsfpixGmql2m2vnRAUh+/fEmZfP/8WAECj1CBCF4Zw56/Q\nmz4O1gTxzw8RERH1azwntgVDbC8ZTFZ8fqoEQf5qzBzXtzDiSvsoMdfDukN65EikR4xETs1FnKnM\nxoSYcW6/ht7SiIPFh3Go9CgMNiM0CjVmJtyB2YOnI8Iv3O3XuzHQWuwW5NZcwtnK87hQ0xJoDxQf\nwpzEGZiXOBM6ldbt129zpOwkPincjTBtKNaO/S/4qTyz7vjGMHus4hSqjbWoNdWh1lSP8ubrHX6N\nSlAi7JZgG64LcwbfUG1Ih+PcRERERN5Cp+WaWIAhttf2ni6B0WzDAzNTodV49gff/Hpu6uRu305b\nhG9q8/Cfgk+RHjkKWqXGLa973VCFfde+xPGK07A5bAhUB+BbKXdjesJUBKoD3HINVzRKDTKjxyAz\negwsditOV2bjk8uf4bOr+3C07CTuS70HkwZlur0rmVN9Ef+69AECVP54Ytz3EKoNcevrdyQhKA7L\nghbf9JjRZkStqR61pjrUmOpawq2xzvnYpda1yLcSIGBUxHB8f8zDUCv4VyMRERF5H+5O3ELyn9Re\nfvllnD59GjabDT/84Q+xf/9+5ObmIjQ0FACwZs0azJw5U+qyesRotuHzk8UI9FNj1njPdmGBlk6s\nVqlBQqBn190OJNH+kZibOAOfFe3H7qv7sTh1QZfPd4gONFmbUW9uQINZj3pzA+pNDahv+9jc8rHJ\nbgIARPpFYG7iXZgyaCI0bhyn7SmNUo2psRORGTUGe68dxN5rX+Cdi+/hi9IjWJa2GENCktxynSsN\n1/D3nHehFJR4bOyjGBQg31nGfio/xAf6IT4wtsPft9itqGvt2taYap3htqSpDLk13+Djgp1YNmxx\nh19LREREJCeNSgFBAExmdmIlc+zYMeTn5+O9995DXV0dli5diqysLPy///f/MGvWLClL6ZN9p0vQ\nbLLh/ruwrWCxAAAgAElEQVSGOBdXe4re0ojrhkqMDB/GUUc3uzt5No5XnMG+a19gVMRwAHAG0oZb\nwmmDWQ+72PlfFgEqf+d46pTYCRgXle5V6y91Ki2+NWQ+psZOxseXd+J0ZTb+eHo9JsaMw5LUhQjT\nhfb6ta83V+Kv5/8Bu2jHD8Y87LZg7CkapRoxAdGIuSVom+0WvHTyVRwo+QrDw4diTOQomSokIiIi\n6pggCNBpVOzESnmxSZMmISMjAwAQHBwMo9EIu71//SuC0WzD7hPXEKBTYc6EBI9fj+thPUer1GDp\n0Hvxj9wt+P/O/LXD5ygEBYI1QRgcFI9QbQhCtcEI1YYgRBuMMG0IQlof07hpHNnTIvzC8F/pD2JG\n/R3Ylv8xTl0/h+yqXMxNnIF5STN7PFZdb27Aa9lvotlqwIMjlvXr4KdVarAm/UG8fOov2Hzxffxy\n8k8kGYkmIiIi6gmdRsk1sVJeTKlUwt/fHwCwbds23HXXXVAqlXj33XexadMmRERE4LnnnkN4uOuN\nb6Kigjxdboc+2J+PZpMNDy4YgcQEz+/2WnqtBAAwKTldtu/Zl82PvAP1Yg2qm+sQ7h+KcL8bfvmH\nIlQbDIVCvo6qp/6bR0WNweSho/Hl1eP41/mPsevqXhy/fgoPZizFHUkTu9VFNliMePnA26g11eE7\n6Ytw3+g5HqlVSlFRQVhtW4Y3z/wbW/Lex/Mzn5b1v/9Awb/baCDgfU4DAe9zaQT6q1HfaBnQ77cs\nu5fs3bsX27Ztwz/+8Q/k5OQgNDQUI0eOxN/+9je89tpreP75512+RlVVowSV3sxsseODA/nw06ow\ndUSUJDVcqLgEtUKFYEe4LN/zQDBn0OzbH3QA9iagpqlZ+oJaRUUFefy/+ejAdDw7OQ17ig5gX/GX\n+MvxTfjk4j4sS1uMlJDETr/O6rBhw7k3UVRfgunxUzE96k6fuT8zQzIxNioH2VU5ePfUx7gnZa7c\nJfk0Ke5zIrnxPqeBgPe5dFQKBYxmq8+/312FdMlbDIcOHcLrr7+OjRs3IigoCFOnTsXIkSMBALNn\nz0ZeXp7UJXXbgbOlaDRYMW9iAvx1nt+sp9lqQFlTBZKDE7lbKnmMTqXF4tQFeH7Kz5AZnYGr+mv4\nw+nX8Fbuv1Fnqr/t+Q7RgXe+/jfy6i9jXFQ6lg+7z6Nn7UpNEAQ8OGIZwrSh+PTK586RfiIiIiJv\noNMoYbOLsNkdcpciG0lDbGNjI15++WW88cYbzt2In3zySRQXFwMAjh8/jrS0NClL6jaz1Y7PTlyD\nTqPEvEmDJbnm5forECHyaB2SRIRfOL6X/hCeznwMgwPjcPL6Gfz22CvYeeVzWOwWAIAoivgg/xOc\nqTyP1JAUPDJqhVdtYOUuAWp/PDJ6BQDgrdx/odlqkLkiIiIiohbtx+wM3HWxkrb3du7cibq6Ojz9\n9NPOx+6//348/fTT8PPzg7+/P1588UUpS+q2L8+VQd9swbemJSFAgi4swE2dSB5pYUOwbtJTOFZ+\nGtsLd+HTK5/jSNlJLEm9B7XmehwsOYzYgBg8lrEaahmPD/K0oaEpuDdlHnZc2YMtF7fi+2Me9qmO\nMxEREfVPbaejmMw2BPr57s9iXZE0xH7nO9/Bd77zndseX7p0qZRl9JjVZsfO40XQqpW4e1Ln6wTd\nLb++EEpB2eXaRCJPUAgKTIubhPHRY7C76AD2X/sSm77+FwAgVBuCx8eugb/aX+YqPW9+8mxcqitA\ndnUuDpUexV0J0+QuiYiIiAY4nZadWN+bA/SAL7PL0dBkwewJ8ZL9a4fJZkJxYymSghP6zfEt5Ht0\nKh3uS70Hz2X9DJlRYxCpC8fjY9f06VzZ/kQhKPDI6BUIUPvjg4IdKGksk7skIiIiGuA4TswQ65LV\n5sDOY0XQqBWYP1m6jujlhiKIEDlKTF4h0i8C3xuzCv8z7eeICxwkdzmSCtWGYNXI5bA5bPhH7j9h\nbl0fTERERCQH5zixxSZzJfJhiHXhqwvlqGs0Y3ZmAoL9peuIFtQXAuB6WCJvMCZyFGYl3Inrhkps\ny/tY7nJ6pdJQhSarfEdGERERkXuwE8sQ2yWb3YGdR69CrVJg/hRp16UW1F+BAAFDQpIkvS4Rdey+\noQsxODAOR8pP4tT1c3KX022iKGJ/8SG8cPyP+P2J/0OtqU7ukoiIiKgP2kKskZ1Y6sjhC+Wo0Zsx\nc1w8QgKk68Ja7BYU6YsxOCgefiqdZNclos6pFSo8mv4gNEoN/vXNB6g21shdkksWuxXvXHwPH+R/\nAo1Cjf+/vXsPj6LO8z3+ruprLuQGCRBFQIiEAYQBYUTU9TaOunrUM+Oio8fB4zgX1plz2GcGeRj3\nAR8OqIzPswycOerqwzgO44oTldVRwUUBHY0gogjILUEC4ZKQCyEh6U53V50/On1JCAxCku5OPi+J\nVfWr6u5fd32rur79+1VVvf84y754jsbWpkRXTURERM5RWrQ7sVpipYNgyOKt0gqcDpObergVdv+J\nA4TsECNzhvfo64rImQ1Mz+fuS+7EF/KzfMdLBK3k/QW0zlfPv235f2w6uoVhWRfxr5f/iu9edA3V\nzTX83y+epznQkugqioiIyDmIXp3Yn7zHId1NSexplO44Sk2Dj3+YUEhuP0+Pvvbe+vD5sEU6H1Yk\n6Xxn8CSmDJpIxYmDvLlvTaKr06m99ft48tOlHGg8xNTBk/nfE39Gjieb20fczJWF36Gy6TDPfPkH\nWnWRKhERkZTjVUusktjOhCyLtz6uwOkwuLmHW2Ehdj7sCLXEiiSl6ZfcQX5af9Ye2MBXtbsTXZ0o\n27bZUPkxS7/4d5qDLUy/5A7uLf4BLjP8ZWcYBtNH3cmkgvGUN+znuW1/SurWZBERETmVLuykJLZT\nG7+qovp4C1ddWkheVs+ekxqwgnx9ooLCzEFkuNJ79LVF5Ox4nV7+55h7cRgOXvxqJQ3+xkRXiYAV\n5M+7SnhlzyrSnWn8csJPuPrCKzAMo91ypmFy/7emM6Z/MV/V7eaPX72MZVsJqrWIiIh8U7Ektu/+\nEK0ktgPLsnnz4wocpsEtl/f8lYEPnKgkYAV1PqxIkrso60LuGHkLjYEmXkxwInjc38CSLc9QeuRT\nLup3AXMm/y+Kck9/OoLTdPLjsfcxIns4W6q/5OXdr2Hbdg/WWERERM6VuhMriT3Fpp1VVNU1c+Wl\ng+mf3fNXBt6r+8OKpIxrL7ySsf2L2VW/l7UHNiSkDvsa9vPkp0vZf+IAUwZNZNbEmeR6c/7u49wO\nNz8fP4Mh/S7go8ObWFX+thJZERGRFKDuxEpi2wm3wu7HYRr8YwJaYQHKokmsWmJFkp1hGNw3+p/I\ndvfjzX1r+Lqhokdf/6NDG1my5VmaAif5QdF/4/7R03E7XGf9+DRnGv88/kEGpuez9sAG3q1Y1421\nFRERka7gdJg4Haa6E0vY5t3VHKltZurYQQzISevR125sbWJV2dvsqS9nYHo+We5+Pfr6InJu+rkz\nmTHmHmzb5g87XuqRW9cErSD/sfs1Xtr9Kl6nh38e/yDXDrnylPNfz0Y/dya/mPAQuZ4c3ti3mg8q\nS7uhxiIiItKVvG6HWmJTzZpPKmjp4vsiWbbNmx/txzQMbp3ac62wx/0NlOx5g3/9+HH+68B6Mlzp\n/PeRt/bY64vI+bskdyTfG3Ydtb56/mP3q93aLbfB38jvPv93/nboEy7IHMzsy35JcV7ReT1nrjeH\nX3z7Ifq5Mnllzyo2H/28i2orIiIi3aGvJ7HORFfgXDz9wSrcjUP4h7HDuX7ShV1y7uqW3cc4VHOS\nK8YOoiC3+68KXNtSx7sH1vPJ4U8J2iFyPTncOPQapg6ejOsbdAcUkeRwy7Ab2FNfzpbqLynOK2Ja\n4Xe6/DX2nzjAc9v+xHF/A5MKxnPv6LvwONxd8twD0/N5eMKPWfL5M/xx50o8Tg/jBnyrS55bRERE\nulZmmosDVU2s3niAGycPwTS/eW+sVOaYP3/+/ERX4pt6rfKPMGA/5bWH+a+Pazh0JEheP8853w7H\nsm2efeMrGlta+fkdY8lM674ksqr5GK/vfYs/7y6h4sRB8tLyuHPkrdw7+gcMzx6Kw3R022tLasnI\n8NDc3JroashZMg2TUbkj+eToZ2yv2YnH4aEp0EQgFMBpOnCaznPq7htRemQzz2//Ey1BH3eMuIXv\nF92G0+za3yGzPP0YkT2cT6s+5/PqLxmRPYz+aXld+hodKc6lL1CcS1+gOO9Z+TlpfLmvls/31rBt\nXy0jCrPJyuiaH7aTRUaG57TzDDsFL0f59p73eWvXOo611ABgNWUTrBrKUE8R35synImXDMBhnn1P\n6c/3HGPZa9u4fMxAfnLbmG6p8+Gmo6ypeJ/PqrZiYzMovYDvDbuOSQXjlbhKp/Lz+3HsWOLvPyrf\nzBfV23h++wps2u9aPQ43ud5c8jw55HpzyPPmkOtpG3pzyPFkd5qUhqwQr5X9lfWVH5HmTOOBMT9k\nTP9R3foevqrdzTNfvoDLdPLLb/+EoVlDuu21FOfSFyjOpS9QnPe8xuZWXn5vL6U7qsIXpp06lH+c\nOgyXMyXPGD1Ffv7prxGUkkksQFV1Azvr9rLh4EfsqNsFgN3qIXjsQrKai7hhwkiuHl9IuvfMLRW2\nbfPYC59ysKqJBT/+DoUDMrq0ngdOVLK64n22HtsOwIWZhdw07HrG54/BNHpHgEn30JdB6jpysopD\nTUeo9x2nznecen99eOg7TnOw8ws/GRhkuTPJ9eaGk9y2ZHfrse3sPb6PwRkD+cm4H1GQPqBH3sOW\n6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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] } \ No newline at end of file diff --git a/dopamine/colab/load_statistics.ipynb b/dopamine/colab/load_statistics.ipynb index 9d91858..1109e87 100644 --- a/dopamine/colab/load_statistics.ipynb +++ b/dopamine/colab/load_statistics.ipynb @@ -1,313 +1,313 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "load_statistics.ipynb", - "version": "0.3.2", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - } - }, - "cells": [ - { - "metadata": { - "id": "VYNA79KmgvbY", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Copyright 2018 The Dopamine Authors.\n", - "\n", - "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", - "\n", - "https://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." - ] - }, - { - "metadata": { - "id": "JFmSgHO3awJo", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# Loading experiment statistics\n", - "\n", - "This colab illustrates how you can load an experiment's statistics and plot it. We provide a sample statistics file for a\n", - "modified Rainbow agent trained on Dopamine. Note that the performance of this sample data is not reflective of the standard settings, it was compiled solely for illustrative purposes.\n", - "\n", - "* Example 1 shows how to use our colab utils to load the baselines and plot them against your experiment. \n", - "* Example 2 shows how to load the raw experiment statistics and plot using a different package.\n", - "\n", - "To re-run this colab, run each cell in order." - ] - }, - { - "metadata": { - "id": "VQBg6yl8Kk1K", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Install necessary packages.\n", - "!pip install --upgrade --no-cache-dir dopamine-rl\n", - "!pip install cmake\n", - "!pip install atari_py" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "w5-Emz9PKoUq", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Necessary imports and globals.\n", - "\n", - "import numpy as np\n", - "import os\n", - "from dopamine.agents.dqn import dqn_agent\n", - "from dopamine.discrete_domains import run_experiment\n", - "from dopamine.colab import utils as colab_utils\n", - "from absl import flags\n", - "\n", - "BASE_PATH = '/tmp/colab_dope_run' # @param\n", - "GAMES = ['Asterix', 'Pong', 'SpaceInvaders'] # @param" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "ALqERTiXNfhs", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Load the sample log files.\n", - "\n", - "# For illustrative purposes, we are providing sample logs of the Rainbow agent\n", - "# trained without sticky actions.\n", - "!gsutil -q -m cp -R gs://download-dopamine-rl/colab/* /content/" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "h9YllEW_bMnv", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "## Example 1: Plot against Dopamine baselines" - ] - }, - { - "metadata": { - "id": "VwGrcoQznHKC", - "colab_type": "code", - "cellView": "form", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Load the baseline data\n", - "\n", - "!gsutil -q -m cp -R gs://download-dopamine-rl/preprocessed-benchmarks/* /content/\n", - "experimental_data = colab_utils.load_baselines('/content')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "Y7d1SpcmMMRt", - "colab_type": "code", - "cellView": "both", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# @title Load the summarized sample data and add it to the global data frame.\n", - "import collections\n", - "\n", - "# Now read the data for our samples in a summarized fashion. The files will be\n", - "# in the local directory /content/samples/rainbow/GAME_v4, so we make use of\n", - "# the parameter_set and job_descriptor parameters.\n", - "parameter_set = collections.OrderedDict([\n", - " ('agent', ['rainbow']),\n", - " ('game', GAMES)\n", - "])\n", - "sample_data = colab_utils.read_experiment(\n", - " '/content/samples',\n", - " parameter_set=parameter_set,\n", - " job_descriptor='{}/{}_v4',\n", - " summary_keys=['train_episode_returns'])\n", - "sample_data['agent'] = 'Sample Rainbow'\n", - "sample_data['run_number'] = 1\n", - "for game in GAMES:\n", - " experimental_data[game] = experimental_data[game].merge(\n", - " sample_data[sample_data.game == game], how='outer')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "gLGwj4ZwntWD", - "colab_type": "code", - "outputId": "35b32680-df13-4708-cb18-6875188bc3c5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1502 - } - }, - "cell_type": "code", - "source": [ - "# @title Plot the sample agent data against the baselines.\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "for game in GAMES:\n", - " fig, ax = plt.subplots(figsize=(16,8))\n", - " sns.tsplot(data=experimental_data[game], time='iteration', unit='run_number',\n", - " condition='agent', value='train_episode_returns', ax=ax)\n", - " plt.title(game)\n", - " plt.show()" - ], - "execution_count": 6, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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YNf/IyV5KxWT38BoYVmzAMTuWDXKdVPvYAt5cafNkyWd4s7GRNYI9vG/5gHeyvPzySxxw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ceoHcUxi7POmciuJdX7LdRwZkn2OEUo2kW84yoapd00Sp+SyF2D7/uUTr6Ea1VI9dxK5Ix02guF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S4ocZD8uzcIT7w2j8Hm6MCO/o4jzQ+DRuMEpJVq/+X3jOIsUT/yPx7JkHuv0RhrHb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\n7E2f8yOFEiwtfVKHS5vU39J3rtRhGIYnbHdECwo2MHU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iOm1FKzQ0VUVTlDTJic2cD/5v/8uZzzU8BRGrtSbPc5rNJlprfvzHf5xWq8VX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\nBoGLwqQuorYQKSKQzgiKXMQUo49TOes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/5f+25/6MSex9IA42WLn0v6PpJnMX/ydMu/K5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\njDHGuDfGJHYPBN3rxIMVCrUXsL17zzh8nBBRRLS6goxiNv7izwiuXVG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4k8t/xvdv/xBb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9r1NjJKhkg6SigZLntbfS59eA93B7KKgjeURBIEHFb9XZF+uBmqqpLKKIQ2zYKqKvgjSUKxNA6r\nHqtJRyKZIRLfatn9LCCrKLj9MfQ6idie+reHzxn84SShaIoDvS5O7m9mdMbH+QlPTRIbjmlp0fuK\nbmcyyNy/v5np5RBXp7xlSezojI+sohJLZJhYCHBwX33jK5mMwuUpL3PuMAvuSMGKCdDVYuHBkVYO\n9Dr5+9cmmMv9vdYcfDkseaNkFbXw+gc7Hfyn5w7x0sfzjM74+NtXxvmdrw4X5uWvTnvxBBIcH2yk\n2WmiyWGko8nM2HyA9WCCpiqLUVVV+cUHs4xO+3jwQAtfe6in7O3Ojq3hDSU4OtDIl+7vKhCWg/sa\nyGRVfv7+DH//2gTPP97Pi+9No6oqv/FEf1l1VSeLDHc7Ge52oqoqr51d4PT1NRY9UYa6HDXfn9EZ\nL4IAX3uom795eZy51XDJPOPtxORikHgyy8OHmhBFga880MXNxSBvnl9kf48Tq0mHqqpcnfbx+tkF\nookMnU0WnjjewUCnfcdkNp3JMrUUoslhpNFhRK8TySoyyXSWI/2NnLmxxrUZP+9eWkZRVL5TFEJ0\ncF8DLU4TP3l7ivVgggO9Lr7xSG/ZDQObWc/9Iy3cP9KCL5TgypSX8+Me3ji3SHeLdQshPH1dmyfd\nTMBsZh0mg1Z5lu/1Lh43efJ4B9dmfLx5doH9XfZb7o7V+mlnWV6PcWygkZ5Wa6H3tBKJPTe+hqKo\nPHigBUEQEBAwGmSMBmkL8c7DUkHdNBnlwsZyZ5OFP/zGwbqO++GDLZy+7uaj0VVO7m+ue5319gWN\nQz19orNwTYmiwFcf7EYnC3xwZZUzN9a2hORdn/WhKGqhJqgcDDqJZqep5rHcfSvCO4hUbHdJbCaT\nJR5NoqTTXDm7SFzRsS86QfuXn0KQJCxmGWPj7Q052G1klSypbIpYOkYwGWY1tqYRwG2svxOZBP86\n+UtimThf6nmCAce+uu8riRINxt17z2RRxmlwaPOum7/HBcomEe+hNrT02Chr/hi+UKLszud2EUuk\nWfJEiSXShOMpljxR1oPx22pbTWeyhGMpQtEUwWiKYCRJIJLEH9b++UIJfKEEnkCcFW+UhbUIc+4w\nK94o0US6LDHNKgq+UIIFd4S1nEX4s0Zg88hklT0Cew/i+qyPH788titJ058WsorC//j5KD9/f6bs\n3/PzsPvabPS0WmlyGLkx6yeWqP49turTNmNbG0oVhcEuB0a9xOiMr1CxVIzLk+vkucTFyfW6X8O/\nvjPFrz+eY3TaRyqjMNzt4IsnO/njbx7kD545wOH+BiRJ5HC/tkDMh8JsF3O596N4Xs2o15S1k/ub\ncfvj/O2rE0TjmqPk7YvLSKLAE/dpzjZBEHj0sLZ4/Xh0teLzqKrKK6fnGZ3WjvPyTS+pMtdZVlG4\nOLGOQSfx7KmeLdbdowONPHuql1giw9+/NkE0keHL93fT31G7v1QQhMLtFj2RmrcPRJIsrkXpbbPR\n3WLFYdEz7w4XRqpuN67c1GZxj+VIgM2s5+kTnSRSWV4/u8B6MMHfvTrBz9+fIZlW6Gu3sbQe5R/f\nmOSvXhrj5lJwR8c6vRwmk1NeAfQ6qRDWdHyoCYBffjCLP5zk0SNtBWtxHk1OE3/0zQP8x28c4Def\n7K9L8W6wG3nyvk5+44l+VFVLDy9eA6z548yshNnXZivZLBIFAWcRKbWadHQ0WTAXPadBL/HE8Q7S\nWYWr0zv7nBTjo9FVRqd9dDZbePZUL62543H7ygs2qqpyaXIdo14qEDqdLCIKQsVZeoHK+RTmGu+n\nKAhlQ7PMRh0n9zcTjqW5nLu2amHeHebmUojeNht97Vs3+h453IZRL/HxtVWSmyrprkxpz3G4v/zG\nncNioK3BXBeZvmdJrKoqpGIryMYmxM3zlztENJxCSaWYH19lOazDnlznwBdGEMwWDHoRs0WHZPls\nqHypbJrlyCpLkRVWo2usx30Ek8GKs66VkFWy/HzqJXwJP/e3HudEy9H67yxAo7Hhtlh7LTozTcbG\nEiJr01nRS59+8fNnDbFEhlVvTJv/SGYIxVL4Qgnc/hirvlhF0pnJKqwF4rh9MQKRJPFkBiXXqekJ\nxFkLlPZ8qmhBR0ueKJ5AnFgivXs9oKqKP5xkeT2GN5TAF07gDyfw50hs3rYbiqUIxVJEE1p1THYb\nNS6fVeK6hz1cmFhnzh1hdiX0aR9KRQQjSRJVNs604JkEV6e9hMvM8hdIbLsNQRA4MdxEVlG5PFWd\nYLoLJLZUbZUlkYP7XIRjaebcpc6fNX+cZW+MwU4HvW02ppdDhKLVR13yQTmTi0H6O+z8yXcO81+/\nf4zvf3GIR4+0bwn1OdDrRBSEHZPYebdG5nraStcsgiDwzMM9PDDSwpo/zo9fGeedS8uEoikePNBS\novru73HSYDdwZar8ew7wzqVlzo55aHWZeOhgS8EGvRmTC0Ei8TRHBxorptee3N/MVx/UUnqPDTby\n0MH6ldG8Ura4VpvE5t/TfBfnQJeTaCKDN5iocc9bRyyRZnJRs2IWX3Mn9zfT0Wjm6rSPv/zFNWZX\nwwx1OfiT5w7xO1/dzx9/6yAjvU6WPFH+8fVJ3ji/uO3nHp/3A1q1jk7SyJYxN8/d1mCmrcGMoqpa\nkNB95cf0dLJEZ9P2Kwb3tds5OdyMJ5Dggysrhf9eUGEPlaqwTqthS1iXLIm0uErJ0cF9LkQBxnKv\nbae4OuXlzfNL2M06vvfUILIs0uw0IQgU5uw3IxRNEYql2dduK1hu85sCZqOMsEVpAaNBqtjpKkti\n1aols1GmxWkqe/9Th1qRRIEPr66U3XQrhpaMvVWFLTlOvcypQ63Ek1nO5JKXQesYXvRE6Wu3la0K\nanKYcNkMdV8f9yyJzSS8qEoKg3l35mHTqQypZIZkKMrodApJSXHfkB65uQ1RFLCYZSSbDWGbQ9Of\nBqLpGO7YGhnl1pQ0VVX59ezrzIUXGXL281TXY9u6v0Nvxyjfvg4vs85Es0kLQpBECYeh9o7tvQKt\nI1QLJQhFNWWyHBkNxVJ4AvGKBC2R0ipPihcwqqoSiCQLKms8lSEQSeL2x1hwR1j0RIhWUftUVKKJ\nNGuBeEHdjCY0S18kng82SlZcNG1GPJlhaT1KMJrcI5p7uCuhqirXZnwEI9uff96N517yaNVGC2vV\nK45qwR9OMnMbiHAgkuQvfn6Nf37rZsXb5NUrVWWL6qKqKrMrYSxGuWB7PTrQhCQKXBhfr6parfq0\nBWo5y3BeDd38fJduasT4+FATDx5qQ1W1dNVK0NTKBa5O++hqtvDCUwM0OYxVF3pmo47+Dhsr3ti2\nyVVWUVhYi9DsNJZVhARB4GsPdfPQwVbWgwk+Gl3FoJO22AZFUeDUoTayisrp62tbHueTa6u8f3kF\nl83AD748zCOH2xAFgXNjni3v+flcbU+tGd+HDrbyv75wlG89um9bRMlk0M790nq05iJ+dNqHKAoc\n6NWcW/05xXHOXZsA3ypGZ/woqsqxwVIrpigKPHOqV1tvGmW++9QA3//iIM6clbqtwcwLTw3yx988\niNOq5/T1Nfzh+r9PFEVlYiGI1aSjs9lSIEuiKKDLkcInjrfT02rl+cf7dpT2XAtfur8Lu1nHB1dW\ncftiRBNprkx5cdkMDBdZwHWSiM1cWZCwFan4JoPMYLeT5fVYiTW/GIqisrAWKbtpraoqH42u8uL7\nMxh0Et/74iDW3HPrZK1ex+2Ll/0OWVrXvk+Lrcb5RF9REMqGItVKO6+mxtpMenSyWNbabzPrOTHc\nRCCSYrTGxtf0coh5d4ShLgfdLZWFuQcPtmIySHx8zU0iZ3POfxeWsxI7LIZth6Pd/YzqNiG5y1bi\nSFi7+KevLZMVdfSzgn1kPwBWi4woi0i2u5skqaqKPxHAG/fdsi1GVVXeXHiPG74JOixtfKPvq3Wn\n/QqCgNPouCOk0iQbaTY10WB03hNpxFlFqaheqrlKlxWvFve+tB5lLRDDF9aUyUVPhFVfrNAfuh6I\n4wslahI/FRVvKIE7d9+l9SiBSHnCqKJuS11VUYkl0ngCcdz+GOvBOL5wgmA0qSmqocqLt/xrcPsr\nq8V72MPdgDl3hJ++O82PXxnXkqXvINaDiYKNeKEOlWozFEVlbM7PP7w2wf/506v83asTO3qcanj9\n7CLpjKJ9b5Wxg8aT2typy2ZAEgUu3ywlpt5Qkkg8zb42W4H4mI0yB/e58IYSVcmJ2xfDZJCwl1k0\n97ZasVv03Jj1k8lo3zFZReHqlBezQWa4y8F9wy3Iksjlycpk+d1Ly5wdW6PFZeK3vjRUM301jzyJ\nrrUo3YwVb4x0RilbWZOHIGizmI8c1oJ2Hj/WXmJzzKtIxwYasRhlzo97mFzUwno+vrbKS5/M8drZ\nRawmHT/8yjBWsw6bWc9Ir5O1QLygBIO2+TG1HKK7xVo1CCYPm1m/o7nPrmYLqbSCJ1BeOQNyvzVx\nBjvthdc72JUnseWzNnYTV6Y0G/rhvq0koKPJwn95/jD/+fnDHOh1lX0P2hrNPHWiE0VRef/yct3P\nu+CJEEtmGO52IAhCyTWYJ177e1z83tdHbnm2tBIMeolnH+lFUVX+/cNZzt5YI6uoPHSwpeS11lLy\nLCZdicp5dEDbGBmvoMZ+eHWFv35pjL/8+TXG5vyFz2l+lvqNc4vYzDp+75n9tDeWpoG3uEwkc/3p\nm7GY2xzsLEoQ1xeFmm0mrJqVuPpnv5LVWCeJhfNkNupwWLaeo/wm0jsXlyo6Q8KxFC+f1lK5K6nt\neRh0Eo8cbiORynL6+po2pz3lRZZERnpLR/csRl3dyeDF+Pyv2isgk9R2PXWmWx/ETybSZNJZMukM\nM4sJJCVFT5t2IRkNEnqdiGSxIkh3b+JtMptiLeYhnNqdxcXHK+c4v3aZRmMDvzn0rbptugZJT5u5\n5Y52xBplAya59g/jnUI6k2V5PVpxFktVVUKxFCveaMk/tz9W1d6q5FI0F9wR3L4YoWiKdEZBUVSC\nOWV0PRgnma4cOpRIaZUqC2sRAtvYxQWI5+57JwljKJbaYkuG3LztepTI3nznHj4DuDihqVCBSIp/\nfmOy7Mzg7UJehQWtz3A7n98zN9z893+7wk/enmJqOUSDXVukTOQSTncDMyshbsz5C8rLmRtbFb/r\ns36yisqJ4Sb29zjxBBIsezfm1PI26c0hTieGtcXthdz7vxnJdBZfOEmrq3zyqyAIHO5rIJnOMrGo\nveabi0GiiQxHcrOrJoPMgV4nvnCS+TLk/pNrbt4rqJVDdc0R5rG/24ksaZbi7WxMl5uHLQdBEPjS\n/V386XePcurwRmqsTpYw5hbbsizy0MFWkuks//TGTX7+/gyvn13k3JgHk0Hmh18ZLlm8PjCircnO\njm2cx/z7v9OkZaCsNXMzunKq0qKnsuNgtMhKDBo5aHGZMRtl5lZ3by42lc6y5i9V8DyBOMvrMQY6\nHQW1b/PrclgNFe3WeRza10Cz08jlKW9dKn1WUfgwZ+Hd36PNwxbbVo36O5cRO9Tl5Eh/A8veGO9f\nWcGgkzg+2FRyLOYaaqUsiSVk8HBOFbwxt/V7KZtVODvmQRIFfOEkP3l7ir9+eYyZFS0R+/T1NZqd\nRv7g2QNl06k35mK3bowseiIIArTnRgEESjcHTJusw0a9VLMCR6+Tys6SWjdZd102A6ZN581hNfDY\n0TYCkRR/8/LYFqU+FE3x41fG8YWSPHa0ra5e4gdGWjAbZD655mZqKYQvnGSkx1ly/Rh0UtXgt2q4\nZ0msmrPKiuKt95fFY9pCeG5ijWRWpDM4gaGjE0kUsJglRIsZ2XX3BQZllAzBZJjlyCru6BrJOupn\n6sElzyjvL3+MXW/jheFvY6ojRVgQBFxGJ62WFnT3+FzqejBBKpNlLRDD7YsVFqyqqhKOaQFHvpCm\njhT/iyczuH1bCVvhcQNx0hmNoMZTGXzhBEvrERbWIvgjSTKf0/nOWCKN26cR/KyizeGuBeLbmmfd\nwx5uJzyBONkK5DCRzHBjzk+DzcDxwUaWvTF+9t50TcvjbiEfdNPdYiWrqKx460uV9wTivHJ6gVRa\n4YGRFv7Ttw/xx988iCQKTC0Fd+XYsorCKzlV4HtPD9LkMHJt1r9llCA/13qkv5FjuQXv5aIwpY15\nWJlyTx8AACAASURBVDt6eWPhWBrwtHXDay0369baYEausLjM2+auTmnk5+JkLpRnaGPhnQ/Fubwp\n4OnyzXVeO7tQUCvLzZBVg0EvMdTlZD2YqDiXVw55FbS3zYpJX342rxibKzzMBrlkgfzQwRYeP9bO\nk/d18OypXl54aoDff2aE//Ibh7coqz2tVlqcJsbmAoRjKbJZhUuT65gMUsG+Wwyn1VDSDVsOelmi\nq8WCw2KoOE8ItediVVXl2rSvkGwMmqolCAK9rTbCsTSByO6so17+ZJ6//MU1/u8XR3n/ygqhaKow\nK3ysyIpZzTZbCaIo8OTxTlQV3q2hxiqKyovvzXBzKURfu42BDgcCAjrdxvVebQ7zduCrD3ZjNsqo\nKtw33FRC/OpV84o/Sw6rga5mC/Pu8JZRphtzASLxNPePNPOj5w4z0utkcS3K3706wfVZPz2tVk19\nrpAA3pq7vt2b2jiyWYWV9RitLvPGPOymvmhBEErstaYKqcSbsdlSLCCUTTRuchq3fG89cbyDJ453\nEIik+PHLY6znNjkCkWQJgd2swuorbJzodRKPHGkjmc7ys/emAThSdP1qc8qmHSdm37MVO4qiXaiC\neGuESVEU0qksqqoyeWMdQVXojkwgNj6N1SKjczmRnXcfgfXGfUTT9S1G8lBVFX8yyGrUzUrUzUrM\njTfuxyQbsOlt2PVW9JKei2tXMclGXhh+rqyiKggiDWYXYsJY0hF7L9h5ayEUTZUkgMZTGRLeLGaj\nFmNfSwVJZbJ4/PEtXwr+cJJYhdCTzxIh3Sm09OQYirI9u/Ie9nC7cWXKy8/fn+FQXwO/8UT/lr+P\nzvjIZFWODzVx6nAroViaiYUgr56Z57e+euC2H9+SJ4osidy/v5mFNW3Tq9ocVB5j85qq8fWHe0rm\nn3parcyshAnHUtsmZZtxbsyDJ5DgxHATHU0WHjzQwkufzHN+3MOTuUWWL5RgcS1Kf4cdu0WrurKa\ndIzO+PjKA91IksDcahirSUej3YAsi8iySCyRRhAE7htq4vVzi1yf9XP/SKlzK59M3NZgwmXXCJIv\nlCjpXm5xmWh1mZhcCuIJxJlcDBRCcPLY12bDadVzbdbP1x7qQa+TGJ/388sPZzHqpS1q5XZwqM/F\njTk/o9O+snO7m6EoKvPuCA02AzazHotJh92i11TBOn8rzAYZUQRy7lqdLBXORy0IgsD9I8289Mk8\nFybWaXIYiSYyPHSwFV2R1VJAoMGuHaOiaKMwlb7bNRu5iMtmwGHVE4lp2QmbN26bnUYMOomFCgnF\nK94YvnCSQ30NBeJhNsgkVY3w35jzM7caLjlXgUiSf37zJqcOtRY2UGohlc5yfc6PQScRiqZ4+8IS\n71xcQhIFDDqpQKDzCbzhWPlk/GoY6XXS1mBmdNrHY0fay9q0FUXlFx/MFMja954eRBQF5FyCbh75\nRN1y779BJ5HNqlU3yc1GHaJA3aMSZqOObz/WxwdXVjhVFOhkKEpLrgWTQUYWxcJxjfS6WPREmZgP\ncN/whuJ/ZkwLJbp/pIVGu5EXnhpkYS3Cu5eWsZl1PPtwb8VeY6isxLr9cbKKSldzeStxHhajjlBu\nU65W+nAeZqNcuI/2Wsurs5Io4rQZWA9uHJsgCDxxvAOdLPLGuUV+/PIY33h0H698Mk8wmuLxY+08\ncbxjC+l0Wg2EYqnC7GsxHhhp5uPRVaKJDGaj1sOdR7PTVFNdroZ7ljWou0RiU0mNcKwsBIlE0rSF\npzE3OjBb9ZjbW+5KAhtORbZFYEPJMB8sfcL/uPrX/M/Rv+XfZ17l3NolliOrmGUjyWyK+fAio94x\nLqxdQSfKfHfo2zRWqKtxGGw4jXYsOjNG2YhO0u0RWLS03nJBC/kgo3ptfJptd8MiFEukCUbvfCDM\n3YZMtvI88B728GlgxRvlVx/NAlri6fL6VhvjxVwVy7HBRiRR5LtPDtDiMnF2zMNb5xdua61HKp1l\nLRCno8lcsNou1BleMz7vRxDY0rmZnx+8uXRrAU/RRJp3Li5j1Es8dUIjSEcHGjHoJM6Pewrfl/nA\npDyRFkWBowONJFJZxhe0DtNoIlNIJdZJYiFxFbT0UoAbc1vn5YqTiSVRq77oaLLgshpK1MsjA40o\nisrP3p1GVeH4UOk8oyAIHBtsIp1RuD7rZ2YlxL+9M40sifz2l4bqmgMtPBYasWmwaw6ooS4nep1Y\nt6XY7ddGSvLnW5ezPDc7jXVZckVBwKCX0MlSRXW6ForPY95WfHJ4gwAKCDQ7jYVNEFEUKs5h5ntC\ni4/PbtHT0WzZYqcUBIHOZgu+ULKs8p4PpclbifOKpE4WC/PDeSs2bCRKr/njZW3ulTC5GCSdUXjw\nQAt/9r1jPHuql84mi7aZNdhYIPM6WUQUhW2H4eRf65P3aZkw71xa2vJ3VVX59cdzXM1VxhTPYhvK\nkC2jvjx5dFoNNFW5dnSSFjTU5DDR1mCuaYXOY6jLwe8/M1KyEbZdRdhapGKP5GzSN+Y3LMUr3iiL\na1EGO+002jcchd0tVn74lWG+/VhfVQILYDfrMOol1jYpsYV52CISu1mJzf+3fOpwvf2tRr1csslQ\n7fqodN4eOdzGMw/3EE1k+Jc3bxKMpnjyvg6evK9SGrFUcaNNJ0s8ekQLfTvc11AI/RIF4ZZV/HtW\nid0tEpvMzS2OX9V60HoCo0hHDuLc14lkunvmLPNIZlP4k7XnkVRVZTIwzSXPKDOhOQD0oo4R1xAd\n1jbaza20mpsL1t+MkiGSjhJKhXEZnNj05XfqZVHGpvts1AzdaXjrCEmqF9FEGimk/bh5Arc/9n8P\ne7hbsOqNYTRIOCw7C3fZLSiKyqWb65y5scZwt5MnjreX7DjHEhl+8tYUmazKI4fb+Gh0lbcuLPHD\nrwwXbrPqi7HijTHc7Sgs1gx6id/+0hD/369v8KsPZrgw5ubRI+2M9Dh3/fUur0dRVS14xG7R47Do\nWfBEUFW16nOFoimW12P0tdu2zHAOdTp4/ewiNxeD3DdUnzJVDm9fWCKZzvK1h7oLASh6ncR9w018\ncs3N9Vk/R/obuDrlRS+LhUUqaBsCH42ucunmOsNd2n/flyNtsiSULOwcVgMdTWZmV8PEckpCHqu+\nOKIo0OwwFs6tIGiEymLSseSJoqJyuK+BN84t4vbHkUShEMpTvLA/NtjIu5eW+fDqKuFYChV44emB\nwpxmPbAadThtBmRJLIyh6GSRkR4XV6a8LHqiNVX0PAnradVuJ8v5oCsdjQ5KVJtyKFaLTAaZcHz7\n9lq9TuLYYCNnbqwRiafpbbXS5NTWU6Ig0OIybZnDtJt1hGOlKfoCAg0VFtb5x9E2MTYIa3eLlenl\nEIueaEHxBEhnFK5MrWM2yAx0akqSLAm57k2RVpcJo14qCXc6c2OtYFVf8WpVcs46Qo/yFT6H+how\n6mVO7m/m5P5mIvF0ySxn3sJpM+t29D4PdTnobLIwNhdgxRulvdGCqqq4fXFOX3dzecpLe6OZH3xp\nqIRs6MuSLXmL20svS4XPv9Oqx78pXV3bjNiofDHqZToapZwtO7ntTed6A8/ysJp0BHP27wa7kVaX\niZnlEMlUFoNe4mxu4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vmz7x3j+cf76e+wk8ooNNgNhUTfSgozaAv/YuWv0tyeQV9b5THq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\nd9rHjEi7iIQRo+tsk5NziHnz/szKle8wc2Y6lZVHGTl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ffCgHHXQoRxxxFJZl0djYyBVXXM348RO46qqf89ZbFeTm5rJmzYf8/vdPsnLl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OTOFtj3/xnuq16Psxc99D2OvufhzHkPSwsuDuyv4cx5D0EgcOhADa+4d3GiyzzXdND3GBjjmJ9z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utSzGtkVyu8+OwlykWdbbvb2HNPF4Ggj3BUvSvSr4zqMivjf49teS6UstpEILYNf2QQ17Wx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\ntiIiIiIiIpLVFGxFREREREQkq/0/UU366JWRdJgAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "id": "MPazOzyFaoze", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "## Example 2: Load the raw data for our sample experiments and plot using a different plotting package." - ] - }, - { - "metadata": { - "id": "0OCpL9IZOF08", - "colab_type": "code", - "outputId": "32279e50-34d5-44fc-dbe9-7b48c611162a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1094 - } - }, - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "for game in GAMES:\n", - " # Use our provided colab utils to load this log file. The second returned \n", - " raw_data, _ = colab_utils.load_statistics(\n", - " '/content/samples/rainbow/{}_v4/logs'.format(game), verbose=False)\n", - " summarized_data = colab_utils.summarize_data(\n", - " raw_data, ['train_episode_returns'])\n", - " plt.plot(summarized_data['train_episode_returns'], label='episode returns')\n", - " plt.plot()\n", - " plt.title('Rainbow training - {}'.format(game))\n", - " plt.xlabel('Iteration')\n", - " plt.ylabel('Return')\n", - " plt.legend()\n", - " plt.show()" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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p6N4XCCtFfAUWA0rypb3tDXo26Xa9cvGgyxeKmZ/XspqYJXvyA0mjOtMPCzSn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\nrRAEEaIoYvuBRvgCiV327K4AwryoZO+AlOkDscV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ZZ57J/PnzAdizZw9Tp07liiuu4JZbbqGmpiYSZYVlmKZmrIuIiG21eZBXVFTw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\nLiIiDqK0OoCpyW4iIuIgCvI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N/d/bWqLXyvH6NrkmZ8Jv+Kmc/367jw0/lLf4GcYtchqX00p5jYdGX5DcLizP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\nn32W//iP/2ircqUDszc4eeyt74gI8uHuOcNb/Lkv9+S6RsW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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - } - ] -} +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "load_statistics.ipynb", + "version": "0.3.2", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "metadata": { + "id": "VYNA79KmgvbY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Copyright 2018 The Dopamine Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", + "\n", + "https://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + ] + }, + { + "metadata": { + "id": "JFmSgHO3awJo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Loading experiment statistics\n", + "\n", + "This colab illustrates how you can load an experiment's statistics and plot it. We provide a sample statistics file for a\n", + "modified Rainbow agent trained on Dopamine. Note that the performance of this sample data is not reflective of the standard settings, it was compiled solely for illustrative purposes.\n", + "\n", + "* Example 1 shows how to use our colab utils to load the baselines and plot them against your experiment. \n", + "* Example 2 shows how to load the raw experiment statistics and plot using a different package.\n", + "\n", + "To re-run this colab, run each cell in order." + ] + }, + { + "metadata": { + "id": "VQBg6yl8Kk1K", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Install necessary packages.\n", + "!pip install --upgrade --no-cache-dir dopamine-rl\n", + "!pip install cmake\n", + "!pip install atari_py" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "w5-Emz9PKoUq", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Necessary imports and globals.\n", + "\n", + "import numpy as np\n", + "import os\n", + "from dopamine.agents.dqn import dqn_agent\n", + "from dopamine.discrete_domains import run_experiment\n", + "from dopamine.colab import utils as colab_utils\n", + "from absl import flags\n", + "\n", + "BASE_PATH = '/tmp/colab_dope_run' # @param\n", + "GAMES = ['Asterix', 'Pong', 'SpaceInvaders'] # @param" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ALqERTiXNfhs", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Load the sample log files.\n", + "\n", + "# For illustrative purposes, we are providing sample logs of the Rainbow agent\n", + "# trained without sticky actions.\n", + "!gsutil -q -m cp -R gs://download-dopamine-rl/colab/* /content/" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "h9YllEW_bMnv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Example 1: Plot against Dopamine baselines" + ] + }, + { + "metadata": { + "id": "VwGrcoQznHKC", + "colab_type": "code", + "cellView": "form", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Load the baseline data\n", + "\n", + "!gsutil -q -m cp -R gs://download-dopamine-rl/preprocessed-benchmarks/* /content/\n", + "experimental_data = colab_utils.load_baselines('/content')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Y7d1SpcmMMRt", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# @title Load the summarized sample data and add it to the global data frame.\n", + "import collections\n", + "\n", + "# Now read the data for our samples in a summarized fashion. The files will be\n", + "# in the local directory /content/samples/rainbow/GAME_v4, so we make use of\n", + "# the parameter_set and job_descriptor parameters.\n", + "parameter_set = collections.OrderedDict([\n", + " ('agent', ['rainbow']),\n", + " ('game', GAMES)\n", + "])\n", + "sample_data = colab_utils.read_experiment(\n", + " '/content/samples',\n", + " parameter_set=parameter_set,\n", + " job_descriptor='{}/{}_v4',\n", + " summary_keys=['train_episode_returns'])\n", + "sample_data['agent'] = 'Sample Rainbow'\n", + "sample_data['run_number'] = 1\n", + "for game in GAMES:\n", + " experimental_data[game] = experimental_data[game].merge(\n", + " sample_data[sample_data.game == game], how='outer')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "gLGwj4ZwntWD", + "colab_type": "code", + "outputId": "35b32680-df13-4708-cb18-6875188bc3c5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1502 + } + }, + "cell_type": "code", + "source": [ + "# @title Plot the sample agent data against the baselines.\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "for game in GAMES:\n", + " fig, ax = plt.subplots(figsize=(16,8))\n", + " sns.tsplot(data=experimental_data[game], time='iteration', unit='run_number',\n", + " condition='agent', value='train_episode_returns', ax=ax)\n", + " plt.title(game)\n", + " plt.show()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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YNf/IyV5KxWT38BoYVmzAMTuWDXKdVPvYAt5cafNkyWd4s7GRNYI9vG/5gHeyvPzySxxw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ceoHcUxi7POmciuJdX7LdRwZkn2OEUo2kW84yoapd00Sp+SyF2D7/uUTr6Ea1VI9dxK5Ix02guF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S4ocZD8uzcIT7w2j8Hm6MCO/o4jzQ+DRuMEpJVq/+X3jOIsUT/yPx7JkHuv0RhrHb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\n7E2f8yOFEiwtfVKHS5vU39J3rtRhGIYnbHdECwo2MHU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iOm1FKzQ0VUVTlDTJic2cD/5v/8uZzzU8BRGrtSbPc5rNJlprfvzHf5xWq8VX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\nBoGLwqQuorYQKSKQzgiKXMQUo49TOes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/5f+25/6MSex9IA42WLn0v6PpJnMX/ydMu/K5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\njDHGuDfGJHYPBN3rxIMVCrUXsL17zzh8nBBRRLS6goxiNv7izwiuXVG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4k8t/xvdv/xBb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9r1NjJKhkg6SigZLntbfS59eA93B7KKgjeURBIEHFb9XZF+uBmqqpLKKIQ2zYKqKvgjSUKxNA6r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\niZnlEMlUFoNe4mxu4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vmz7x3j+cf76e+wk8ooNNgNhUTfSgozaAv/YuWv0tyeQV9b5THq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\nd9rHjEi7iIQRo+tsk5NziHnz/szKle8wc2Y6lZVHGTl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ffCgHHXQoRxxxFJZl0djYyBVXXM348RO46qqf89ZbFeTm5rJmzYf8/vdPsnLl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OTOFtj3/xnuq16Psxc99D2OvufhzHkPSwsuDuyv4cx5D0EgcOhADa+4d3GiyzzXdND3GBjjmJ9z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utSzGtkVyu8+OwlykWdbbvb2HNPF4Ggj3BUvSvSr4zqMivjf49teS6UstpEILYNf2QQ17Wx\nzRJGep7CiaM4C2VUt5vIfYcJ33MIKRDAtSyqk5OUTx6neuki+twsrnFFGAuKgn9omOCOnQSGt3nO\nxD4fcjyOFLzWya5sVvizE/+NudICTf4ErYFmWoPNdITaiPoiBJUgAclrzl21dGYKs8wU55guzrFU\nXsbFpcmf4KHO+9meGEEQBBRJIaJ49bLvNmau66LbOrptokoKqnRzY9y4ENwYayYQxYqB61J3XvUp\nXr3N1c64Tu3vm5lUrDk/SqKwrn4UoFgxeP7YHD98bRrL9tw/11pstDcF2DfczL6hZvo7IvWalngi\nyBunF3j19CJvnF+uGw1txI6+BEd2tzPcHUMWvcijVOtX6lOurVlczVf5i38+y+XFAvGwyq99YoT9\nQ83X1OJXNJNMUeP8dJZjF1e5NJfDdT2zjd0DTVyazVGopfOuOa0qssjTR/p57GCX16rFddEMm4rm\n9dRzXK9+dTnrpQPnSgY7+xN0t3jtKKJBHy4uqzmNb78wwduXVq7p2eiTRbb3JXjyUA9DXdEb7sWq\nGRaSKGwo/hzHO861NGJB8NzFEQR8snhLph4fxHlo2Q6pbJVkVL3pnrQN3p3GPLr1aYzh1qcxhnc2\nDWHb4KawbceL1GaqvPSTS1TKBjv2dbDrQAfReAD1faiR+TAmEb08x8rEP+LYGpHWI4ST+5DV5nVi\nrjIxxsKf/DFOpYwUi2HnvbRLMRDAPzyCMT9/JSILyMlm1O5ufJ3dqN3dKC0tCKKE4PPh+BVsn4I/\nGF4XNV3DtE3++PhfMlWYIaHGKZtlDGfjejxV8mE6Vj1lWECgI9TGgdY97Epuxy+p+CQfftlPYBOH\n49tN40Lw7tiOQ6Fski1qjM3luDCTIxxQ2DuUpOVdnEDXUBWp3rZDM2yqho1p2fX6WVWR8Skifp+M\nZTt86+fjvHVxBb9P4guPDNDTGubSbJ6zkxkmFgp10RYJKgx3xRjtjbOQqfLqqUUs2yGgyuwbTiKJ\nQr1dS9WwaIsHeOxgNz2t4ZuuXXQcl+++dJkfvz6N61J//4dGW+hvj3JmMs3J8TTnprN119LOZJCH\n93XysQNdqIqE47qcHF/ltbNe78RoyMevPOqZNG0kHl3XrbdAMS0H3bSxbZdIUCESujYt27RsfvbW\nHC8cnyceUelvj9DXHqGjKYhUcxL9IFwkb5UP6jy8Xv11g1ujMY9ufRpjuPVpjOGdTUPYNrhhTNOm\nkK2SXinz6nPjVCsmuw50sutAJ7FE4LalHr+TD3oS0QqXWZn8Jq5jEQk+gF8ZQGluQQqHEWqRpvLZ\n0yz8+Z/hGjrJL/4KsUcfQxsfo/jmG5RPn8Qpl72I7MAggW3bCIzuREkmESQRQZKoOCbPpV6jiokg\nikiIiIJIS7CZQ637CfmuRGxtx+a/nvprzmcusaNpG7+z699iOQ6r2ioLpSWWyssUjBJls0zVqlK2\nqiiiTE+4i/5oD8OJQWJq9Lqpy+8nd+OFoFQ1qWgmsbC6oXOm47rkijqL6XLdxda0HWzbQRBAFiUk\nyWsfUSgbnJ5Mc24qe00ktL0pyJ7BJgY6omRLOsvZKis57yeoyvS0helpjdDTEkL1SSymK4zN5Rmf\nzzO/UgY8gZqM+knG/CylK8yvlmlLBPjSY0N0tYRRFameilvRLM5OpTk7mWV8Pr+u9UU05OOBXW0c\nGGnGp3hRYNUnEfDJBFTptqSdXl4o8Nzbc5wYX6FaaxO25jQLXhubXQNN7BtKsn+kZUMhaVpeurEo\nCjRH/bfd5dFrYO+5r9q1f8PvqDW+E7kbz8OPEo3x2/o0xnDr0xjDO5uGsG1wQ+iayWqqxNnjC0xc\nXAEX9hzqYse+DuKJINIt9HO7UT7ISaSSu8Dq5LfBdeGUhH70Mq7j4OvsIjAwSHDXblzLIvU3X8N1\nHFp//TeJP/Kxdc/h6DrazDRqeztiIIjwDtdizdL4v4/9BXOlhQ2PoTXQzBO9j3K44xCCIPD1c9/g\nrdQJBmN9/OH+f49vE3FqOZZXU2ubgEBA9iKzdwJ304XAtByvmftCnlS2ylBnlI5kiHjYM/1xXZdC\nxeC1MylePDFPKlu94eeOBBV2DTSxqz9BrmRw+nKa8blCvZfj1fgUEcNcb+S1ln4LXupqT2sYSRRI\nF7y+kWvsGWzi8w8P0J4IEfRvLsaqmsXF2SznprN0tIQZraXZyvKVNjnvV2TOMG3eurjM6+dSzK2U\n6W+PsHsgyWivVwMcCSq3bEr0UeVuOg8/ijTGb+vTGMOtT2MM72wawrbBdamUdc4cW+DM2/PomkUk\nqrL/cC+dvXFiicBN9aS9FT6QujCtSPr0d9GlaVzLwfzREs6chpxsRlRVjMUFrnaXERSF9q/+ByIH\n77mp1zFsgz85/pdMFWbZ37Kbe9sOoNk6mqVjORYXsmOcz1wCoDXYQleoneMrp+kKd/AfD/5evZfs\nVuP9GkPbca7b/uB2pkXmywanxld56eQCE7W+oQB9bWF2DyQ5ONrMuaksL55YYDWvIQgw1BklFPBE\nmFzrneri4thuvQ+hIouM9sYZaI8SUGV8ikRFtzAtm4pmcm4qy2K6QjLmpyUeoC0RIBJU0A2buZUy\ns8ueEVKhbNDXFma4O8ZgRxT/VRFE07JJF3Qcx2VnX4JYeGvUSTuuS1W3UCQRRRYbKa7vgcaCbGvT\nGL+tT2MMtz6NMbyz2UzY3tn5VA0+MIp5jZ989wyrqRKSLLLnni5Gdrbi8yvEav0ftwqOoQMCgiR5\n4SygOnGJ7IVnsZqzCH4Rt2hiv1ol3Hcvoaf24uvuRpAknEoF7fIE1YkJrFyWpqc/T2h0+029vulY\n/PnJv2KqMMvu5A5+Z9evIYlXUjdN2+RwxyFmSwu8MPsLzqYvslxZoSXQzP944N9vWVH7flGqGrxw\nfIHRnjhD3bENnUh1w2YlV0UQBcIBhZBfvm6Uz3FddMNGM+x6mqlmWIzN5Xnl9CLTKc9MrK89wkh3\njEuzOaZTJaZTJX54dBrwWtvsH2nmY/u76GkNe31SBWptS7zXWdsnWRPeqiKuS+WNh32UqiaSKHJo\ne+u6YxQFgYAqEwpAJKQy0h2v9w0WEPD7JPyqTMAnrTtH+9tBEoUtdd6KgnBdl+QGDRo0aNCgQYPN\naAjbBlQrJs8+44nazp4Y+w/3kkgG8QcUfHd4LdnVaNNTpL/3DOXTpzw1IQgIqozYH0Q6EEHs9oEB\n4nyYWN/HCf7RqOdQ/I5IoL+vn/hjj9/SMZi2yf9z+uuM5y6zLTHEV/f8xjpRC6BICoqksKNphB1N\nIyyUlji2fJKHug4TVm5/T8StzPnpDF//yUWWs1VUReJXPjbEo/s714nWQtlgYbXM25c8B19J9Pp/\n+n0S8YifXf0JYiEfgiAgCgK6ZXttZHQbx3XIFHUm5gtMzOeZWipiWl7a73BXlIf2djDQEcMnizyy\nr5NMQePsVIax2Twt8QCP39NNb1v4PbnCCoJAJOgjFFDIlwzKmknAJxP0y9cYM7k1Z1/Hdeu1rw0a\nNGjQoEGDBg0awvYjj2nY/PSZs6wslejqS/Dxp7YTDCmI10n7/CBwXRfXMXDsKo5VxbYqOLaG65jr\nfqxckcqxc2jnJnCLFkpHM9JAGLfNhRYBQRbABZ/VSXLXF1DCydt+nCWzzOnV87yycJSpwiz90V5+\nb+/vbOh8/E46w+10httv6zHdqbiul5J7vWiqYdp864UJnj82h+vCaG+cifk8//CzS0wtFvjy4yME\n/TKpTIVfnFrgxRMLdQOijehuDTHaE2dbTxzNsJldLjG3UmJuuUypesV1ujnmZ6gzyv6RFoa7YgT9\n8jqjoK7mEDv6EpiWi+q7tp3Ne0EUBBIRlURE3fQ+giDUe8Q2aNCgQYMGDRo0uEJD2H6EsW2bf/3+\nORZmc7R2RvjkL+1EDXz4qYCu61LJniG/+AKWkb2xB+0EdWfXupsEBGQ1STC+g1DTXhR/800dw3Jl\nhZxeoGSWqZgVylYF14WgEiAoBwgpAWzH5cTKac6kz1MyPWfa/mgvf7Dvdzc1f/qocmE6w/demSJf\nMnhgdzuPH+wi+I7UU9NyODWxyrd+PsFyrko8rPKlx4bYP9zMxdksf//sJV45s8TcSpl7t7fw8+ML\npAsaqiLx+D1d9LZFPFddFyRRZLVQ5dRE2hOyy2Wee3t+3euFAwo7+hIMdkYZ6orS0RQiEvRtarQk\nCF4vUqUxczZo0KBBgwYNGtxRNJZnH1Esy+bFH19iajxNU0uIT39xz4cual3XpZofI7fwPKaWAkHC\nHxlClEOIkh9sETtdwJieRZ+exSlrYLnIrUmC9+xC7mzCtgo4loY/0kcwtgMl0ILjOlQtDc0oods6\ny+VVCkaR4fggzcGmdcdgOzbHlk/xs5kXmC8t3vCxq5LKwda9PNBxL9sTI3dExPtO4dJclmdenOTi\nbA7wyp6feekyPz46zcN7O3jiUA/L2SqvnV3i2KUVNMNGAB7Z18mXHxsmUBOZ+4db6GgK8bfPXuT8\ndJbpVBFBgEOjLTy6v5N4xE+oFmG9ui3PUw/0M5MqcmYyw+WFAkG/THdLmO7WELGQD1EQCQVkokFf\nIxraoEGDBg0aNGiwRWm4In8EqVZMXnlunLGzKaJxP5/7N/uIxAIf2vE4tkY1P45eOEEpexmAUNNe\nYu0fQxCCVM+dpXjsLarnz2NlMwCIwSCh3XuJHnmQwI6diNLGgsS0TX46/XPGcpdZrWbI6fm6+Q5A\nW7CV7U3D7E7uIFVZ5rmZX5DVPQE2mhimNdCMT/KhSiqq5MNxbcpWlbJZQbM0TMdiW2KIQ237iPqi\nd4WT68JqidfPL9PdEmLvUPOGvVuvZjlb4cK01wu1qFkYa21ogLJmMZ3yzsmBjgifOdxHd0uYF08s\n8ItTC5Q1a91zxcM+Do228vC+Tnpawxu+nmZY/Oi1aaZTRR7Z18lAR5RwQLmuKC1UDHJFHdf1Wuio\nioTqk/D7pNuaUrzVaThBbn0aY7i1aYzf1qcxhlufxhje2TTa/TTAdV2y6TIv/uQSS3MFIjE/n/3V\nPSSSH7xhkW2WqOQvUM1dQCtNgesZ9vhDg6jmEPZMBm16iurFi9hFr92KoCgEhkcI7T9I5J5DSLHY\nuwrJjJbjr8/+I5fzU95zS36aA00kAwkiSpjZ0gJzxXls90qPUFmQubf9AE/2fYy2YMu7vwfHxsFF\nuYE62jsF13UZm8sT9Mt0NofWmQ+Nz+X58evTnJxI4zjetBAP+zgw0sLDezvoaQuznK0ykyoxu1xk\nbqXM1FKBQtnc7OUAGO6K8fmH+tk1sL622bQcXju7yGtnUrQ3BTm8q42RnvgNGSKtORq/01zpuo9z\nXKi5FjfYmMbFfOvTGMOtTWP8tj6NMdz6NMbwzqYhbD/C2LaDbTmkFvK89OwYhZxGW2eUJz+/4wOP\n1JraCoXUa5Szp+piVlaaYVnAObNM9fwcrnlFKAl+P8Fto4T27COwaxdyJIoUuP4xn109z9+e/ydK\nZpmBaC+fG/wULcFm/LIfVfLST8HrN3spO8GFzBghJchDXYeJ+DaOFG51UtkKX/vBecbn8wAoskh7\nU5DO5hCZgsbYnHd7Murnvh2tLKyWOTedrbsEK7JY//8a4YBCX1uY/o4oQ51R9oy2UixUkUURSRIR\nBQHV10jv3Uo0LuZbn8YYbm0a47f1aYzh1qcxhnc2jT62HyEcx6Vc1LEsG9ty0DWL1eUSb708haHb\njOxs5aEnR/B/gDW1emmWwvIrVPOXAJB9TUjFJvQ3pimdfrPenkdpbsHX1Y3a30dgcAh//yBiILBp\nVO7o4lsslJbwy36Csp+AHGCuNM/zsy8jCiJP9D7KZ/qfQJU3dpr1ST52N+9gd/OO9+29fxBYtsPY\nbI7LiwU6kiG29cQJ18bXcV2efX2Gf355EtNyGO2NE/LLzK+UmV8pM7vs9Wvtb4/w+D3d3LujFV+t\nz2q+rPPWhRXeOJ8iXzJoTwbpaQnT1x5hoCNCU9S/bmxakmEk567aK2vQoEGDBg0aNGiwBWgI27sM\n23YYP7/M+LlligWNclHHqLVBEQS458E+Dh7uRf4ATHIcW6ecPU1p9Thm1TNikoUm3DGbyktncapV\nAJT2DiL33EvfZx6nKAYRlesL7rJZ4e/OfZPT6fMb/j2uRvnNHV9mtGnk9r2hO4hixWAmVWJsLsfF\nmRyTSwUM86qUakmguyXMcHeM8TmvP2tQlfmNT2zjwT0ddTGqmzYzqSKyKDLQGb3mdWIhlcfv6ebx\ne7o/sPfWoEGDBg0aNGjQoMHN0hC2dxFa1eTV58e5eDoFgCgKhCIqyZYwoYhK33ATQ6OtSPL7Z5Tj\nui56eZZy+gSV3FlcxwQExFIY441ltPOeOZQYChN9+BGiRx4iMDSMIIoEWyKUr0r7OLN6jpJRYWdy\nlKh6JeXgYmacr5/7BnmjQGeonYe6DuO4DpZjYToWsiDyUNcDBJUPzxDrduG6Lum8xnSqxEyqyNRS\ngdnlErmSse5+zTE/fe0ROptDLGerTC8VmFoqMrXkfZ77hpP89qe2Ewuvj1yrisRId/wDez8NGjRo\n0KBBgwYNGrwfNITtXcLibI7nf3iBQk4jFFE59GAfLR0RZFlCkgREUSAY9r0vbWhc18WozFPJnqWS\nO49temZPaCL2uTLmyVWo2CBJBHftJnrkIcIH79k0MpvVcvzjhe9wLnMR8PrRtodaGYkPIgj/f3t3\nHiVVeed//H1rr+qq6n2hgW52aLvZEUFEATVGJ2gUUTTGMWMSMxljZPSH0ZNfIMeTeMbkzDgaJ3Gc\nqGM0PxlJxjD5uY1r1LAoIPvWzdYbvXd1V3Wtt+7vj9b+TQuISZDqgs/rHP6oqltV39vPebrvh+e5\nz2PjDw1/BGDBiHlcNeZyXA7XKT+nTLEsi/rWMOt3trCvoZvmjgjRj0bcPxb0OTlnVD6jygKMHpbL\n+JG5BH2uYz6nJ5JgX30In8d+zMJNIiIiIiJnEgXbLGdZFhv/cJAtG+qx0hZjJxUzZ+EYAp+49/Hz\n+N5EXxN93bvo69iBafaPDFoJC7MuTHpfL+mGGDafD9/YanwTJxE4bw7O/P59Y820STKdGrSicNpK\n81b9u6w98DJxM8HIwHDG5FZyIHSYxleXPOMAACAASURBVHAzzZH+kehcV5Cbz7meSVkyzThtWSdc\nhTeZMglHU7SHomzZ384He1ppD8UGXi/M9TBxZB6VZUFGDwtQWRYkN+fkQd4wDHL9bs6tKjll5yEi\nIiIiMlQp2GaxeCzJqy/souFQF16fk3mXjGNcVcnnFmjTZpxktIVoaB+R7l2Yif79Xq14mvTBCGZt\nGKs5hWfUaHwzL8D31Wrco0YP2mM2biZ4u/49Xj3yFtFUFL8zh1x3kHx3HpF0mINd9bjtLq4Z9yUW\njrxgYPXieCrOvq462mOdnFc2c0hPM7Ysi8a2CFvr2tlW10FdYwgMA5fDhstpx+WwkU5bhGPJQffF\nArgcNqaPL2LmhGKmji8ix3P6FvgSEREREclWCrZZqrM9wsu/2UGoK0rZ8CBfXFKD13dqp+Qm451E\nOraQiLaSjLViJkL//0UTzLow5v4wtj4fOdVTyLlyMt6qanqtKNFUDK8riPHR1OekmeTdpg28fOh1\nwskIHruH0cFKQokQRyOtNIb7F5eqKazihknXkOfOHVSL2+FmcvE5p/T8Po1lWXSHE+T6XSccbT3U\n3MPG3S1EYikSKZNEMk08adLYFiEU6b8H1gBGlvpx2G0kkibxZP9xhgGl+T78XicBn5Ogz0VVZT7V\nowtwnYaFvUREREREziQKtlnowL423vj9HpIJk6ppw7jwC+NP6b2zlpWmt3U9oea3sKwUADZHDg6K\nMJt6iW+rJ30ogiOvgMLLlpB74UXYHA6O9Dbw6t7VfNi2A4v+LV+cNidBV4CEmaA3GcZlc3FZ5SIu\nqbhoYNQ1baXpTUQI5DmxRT2n7Dz+XA2tYX716l72N4QI+JxMG1fEnHNKmViRT9JMs25HM29uaRrY\nJueTfG4H504qZtr4YmpGFxA4xf/hICIiIiIigynYZpF4NMnGdw6yY0sTNsNg/hfGUzNj+Cn9jkTf\nUTqP/BeJaDM2h4+A73zim+uJbNhMqrsLAEd+AQVLvkTuRQvB6WBvVx2vHH6D/d39Kx4P9w9jVLCC\nULyHUDxEKNFLykpxScVFXFqxAL8rZ9B32gwbue4Axf4AbdHMbYYdjaf4zdt1vLWlibRlUVkWoK07\nyjvbmnlnWzN+r5OUmSaWMDEMqBldwEXTyynN8+FyfjzN2I7HbT/hKK+IiIiIiJx6CrZZwDRN9m5v\n4f13D9EXTuDNcXLpVecwvCL/lHy+ZVkkY61EOj6kt20jYGEP55F86yith58CwHA68U2ZSnvVMPYO\ns9OarKdt0z/RFe8mbfXfJzoubzRfrLyYSQXjP9eFq06VaDxFW3eUls4+GtoivLWlkd5okoKgm69c\nMoHpE4pJmWn2HO5i/a4Wtta243TYWDh9OItmjKAwN/OjyyIiIiIiomA75LU09fDHN+o42hDCMOCc\nacOYs2Asbs9f3nTJWDt9XTuJdGwnlewEwIqkSb7eQrr+ANjseCdOInDubGzTaviP+lfY0rYZWvvf\n73V4Kc8poyynhAUjLmB0bsVfXNPnxbIsGtoi7DrUyZ7DXdQ19RCOJgcd47AbLD5/FF86vxKnw/7R\nczZqxhRSM6YQy7KyIrCLiIiIiJxtFGyHKMuy2LzuCJveO4RpWpQMC3DBpeMoLc/91PeZqT7i4XpS\niW5c3hJcvnJsdjeWZZEOh4k21xLp3EHCasLyxPu/K5UmfTiKuT+MWR/DO3YCwa+eh3/mLBx+PwdC\nh3hix+N0xUOMCo7kmnGLKcspIcfpOx0/ij9JPGnS3h2ltTtKc0cfLZ19tHVHqW8NE4mlBo7LD7ip\nqsynOM9DcZ6X4jwv44bnUhA88SisQq2IiIiIyNCkYDsEhXtivPZfu2muD+Fy25mzYBTVM8qx24+/\nWm40tI9oaD/xyBGSsbbBL1pAn410UwSCBrZSD7jBMi3SB/tIHknQHoUDOSmaRjponp4Pnh7G5O6n\nqtMg1hrjlUNvYAFfrFzEFaMvxW4bGqv29vQleG97MwebemgPxejoidHblzzusQGfkxkTiqkZU0D1\nqAKK84budkEiIiIiIvKnUbAdYmp3t/D2y/tIxE3KRuSy4PKJ5Bcef2TUSpt0Nb5CuP0DAAybE6e9\nFPNwL/G99Rj5dmylbowSN7bxXqy0RbjLpLk3zX4zzWF3mp7JDjAM8t1F1BRVUZ5Osa/rAHs697On\ncz8AQVeAv6m+kfH5Y0/bz+FE0pbF7sNdvLmpka117Zjp/tWXbQbkBzxMrMjpH4HN9VCU66Uoz0NJ\nnpeAz3lKV44WEREREZGhQ8F2iDjaGOKDdw9Rf7ALu93GnAVjmHbeyBNOfzWTEdoPPU88fASnuxhn\nawnhNz4g2rQXgFR+gBZHDvvsCQ5ZCVwBJxErTRSwBW34HF4CrgJmFoxnVul0KgMjBn1XV6yb3Z37\nCMV7mT9iDn5nznHrONXiSZNQJEFPOEEoEqc7nKC7N0ZXOEEokqCpPUJXb/8U6tICLxdNLWfGxBIK\ng27sCq4iIiIiImclBdsMsiyL5voQ7797iKYj3QCUlgdZcMVECopOHCQTfc201T2HmeqFVjvh32/B\niibAZqNrXBnvjklzoNACI4XPkcOE/LFUF1ZRERhO0BXA5/DisH960+d78ji/fPYpPd8TCUUS/O6d\nA2zc00rf/7gP9njcTjtzq8tYOGM4Y8uDuu9VREREREQUbDPlaGOIdW/UcbSxB4DyijzOvWAU5RV5\nAKQSPUQ6txLp2o6VimHYnGAZpPvipG0RLJtFakMX5qZurMJ86mpKeHt4jLAvjdfh4YKSqcwomcLY\nvNE4bEOzmcPRJC+uP8zrmxpIptLk+d2MGJlDbo6bPL+b/ICLgqCH3BwXwRwXuTluvG67wqyIiIiI\niAwyNBPPGSzU1cf6tw5wYG87ABVjCph1wShKy4NYVpq+7r2EOzYT66mlf+UnB0bSRjrVC0YaHAbE\n07DdILd8AVtnpflt73ogQYm3hC8Mn8P5w2bjdWZuj9XeaIKunjgWkE5bmKZFMmUSjqUI9yXojSbp\nDidYv/MosYRJMMfF0gWVXHvpJLq7IhmrW0REREREspOC7WkS7Uuw6b3D7NzSRDrdv33P+RePY9iI\n/u17UokQ7Yd+QyLS0P+GsJ3Uti5SOzogaYHdjm/CRPwzZuGfPgNjboBf7/kN649+gN+Zw7KJVzOl\nqDpjKxYnkiYf7GnlvR1H2XOkC8s6+Xv8XifXLxrNwunDcTntOB26R1ZERERERP50Crafs75wnA83\n1LNzSxOpVJpAroe5C8cwZmLxwJTavu69dBz8LRZJzNowqQ+6sToS2PPyCMyai3/yVHyTp2D39m9R\nE01FeezDf2N/dx2lvmK+PeVWinwFp7z2tGXR3B6hszdOV2+c9u4oHT1xUmYah8OGw27gtNsIR5Ns\nre0gnjQBKC/KobI0gNNh4LDbcNhtuJx2cnNc+L1O/D4nfo+TskIfbufQ2DpIRERERESyl4LtKdDe\nEqa1uQeny47H68TtcWC329i1tZndHzZjmml8fhfnzamgelo59o9GJs1EjLYt/4eEqx4rlSb1TgdO\nawR5l32BnHNqcJaWDrqfNGEmqe0+wJr9a2npa2Ni/ni+OfmreBynbtqxZVkcOtrL+p1H2bi7lVAk\n8Znel5vjYv6UYcyfOoyRJYFTVo+IiIiIiMjJKNj+mVIpk7rdbezY3Ehrc+8Jj/MH3cyYW8GkycOw\n2SBef4TQvl1EOndi5vdgK3aS7k7iai6jbNk3cJcPH3ivZVkc7qlnZ8cednfu53BPPabVPyo6v3wO\n1038Mjbjz5++m05bdPbEaO2O0tLVR1N7hK21HbSHYgB43Q7mVpdRVuijIOAm/6N/TruNpJkmkUqT\nTJnYDRuVwwLYtKiTiIiIiIhkgILtZ5ROW4Q6+2hrCdPSGGL/rlbiH21NM2JUPuOqSjDNNPFoklgs\nRSKWYtjIXMZNLCS2ZxdH/+0Foi17sI11Yx/nxyiyYcOJrSdA6eQbcC8sG/iuWCrOBy1beLvhjzRF\njgJgAMNyyhifP4bJRedQVTDhuHVG4yn2HummsT2Mz+Mk4HX2T//1OukKxzl8tJf61jANbWFau6KY\n6cE3w7ocNs6dVMKc6lJqRhfqvlcRERERERnyFGw/RbQvwe6tzRzc105nW4RUKj3wmsfrZNp5I6me\nXk4wzzvwvGVZpLo7CdW/S6ytlob6TsixYcxy4LIXA2BYbnzBaoLD5+L0FAJgpk0O9hxhc8tWNhzd\nRMyMY8NgSlE155ZNZ0L+WPzOY/e2TSRNDjb3sPtwFzsPdXKwqYf0Z1i4yemwUV6UQ2m+l9J8L2WF\nOZTke6koCeB26b5XERERERHJHgq2n2BZFi1NPezY1EjdnjbSaQvDgLxCH8WlforLghSV+iktD2J3\n2LBSKXo3byKyYxuJxgaSzi7s5+ZgCzqhAGy4IWUjbfcT8wRJ5IzE8A4nYnfSGQ1xpHUnuzv3URc6\nRMLsv5816AqwaOR85g0/j4AzQDRuEo2k6Iz3Eo2n6OyNU9cYorYhREN7hPRHSdYwYFRZgOrRhYwu\nCxBPmvT0JemJxOmJJAn4nFSWBagsDVCc58Vm09RhERERERHJfgq2/0Pj4S7WvVFHW0sYgNx8LzUz\nhzOxphS3xzno2MTRZjrf+QM9f3wXs7cXI8+JY34hzoo8rDR0N8OOJGwNJAkTB3qAJmDPcb/bkQyQ\nnx5NPiOwWotYvyfFq5FthKNJTjQAa7cZVJb6GTc8lwkj86mqzMP3iTpFRERERETOdAq2QG8oxh/f\nqOPA3jYARo0vZPLMEQyvzBu0KjFAb90muva9SDrdB34D+1/l4fKXY7lTgEWjafBiOEynx8JygxXz\nYUVLSEf9WHEP2CwMI43dmcbrMTDifmKdefSGXfTSH30hhNNuI5jjpDI3QI7Hgc/jJMfjIMfrJJjj\nYvSwIJWlfpwOTRsWEREREZGz21kdbFMpkw/X17N5/RHMVJqSYQHmf2E8JcOCxxwb72ygbdtzpHP7\noAxseMGyYdgdJNIm3WaKd6IJahOQ7CzFERrJzOGTCHq95BR8FEo9ToryPBTlesnxOAaF5mQqTW9f\ngnjSJDfHhdftOCZUi4iIiIiIyLHO2mBrptL8fvU2mutDeH1O5l42gQk1pceEyVSil/YPVxO3N2Lk\nGlhdaXJLLiK3egExM87j259mb1ct+a4CIvUT6WsspLK4gNuXTqYw97PvL+t02CgInrr9aEVERERE\nRM4WZ2WwtSyLN/7vbprrQ4waV8jFi6twuY/9UfQ0vE9340vgAitk4rEmUDz/emxOJx3RLh7d+kta\n+lopdVTSsGECqaSdS2aO4LpF43DYtU2OiIiIiIjI6XBWBtv1bx2gdncbpeVBLr3qHBzOwfepplNR\nWrc9S8JowrKlsR3wUr7gb3EV9m/Xcyh0hJ9ve5JwMkKJOYlDGyvwuJzcdnUVMyeWZOKURERERERE\nzlpnXbDdsamRDzfUk5vv5Yqlk48JtX3tu2k/8Btwpkm3xgn4zqfgmisGpii39rXx0JbHSKVTBLun\ncXhfGWUFPr577RRKC3yZOCUREREREZGz2lkVbA/ub+fd1/bj9Tn50vVT8HgHb43TuedFwtEPsGwW\n7E5TdtHf4hk+YuB1y7J4ds8akukkjsbptDSWMm1cId+8shqP66z6UYqIiIiIiAwZZ00aazvay3//\nbhd2u40rlk4mmOcdeC1tmrS890uSgaNYkRTujjGULP0KNufg4Lu+eRO13QdJd5cQbizhqgtGc+W8\nUVq9WEREREREJIPOimDbF47z0m+2Y6bSfHFJzaDtfBIdHRx991+gwsLqNckv/CuCF5x3zGf0JsL8\nx97fYZl20vXV3L5kCtPHF5/O0xAREREREZHjOOODrZlK8/JvdxLpTXDeRaMZPb5o4LXw9g9p27sa\n+1gvRGwMm/wNXPnDjvs5P9vwHAkrjnH0HO5eMpfxI/JO1ymIiIiIiIjIpzijg61lWbz9yj5amnoY\nd04J0+dUDLwW3rmNtrrnsI/1YU8FKTvvm9idxy7+ZFkWT77zDg2pfdCXy4ovXENlafCY40RERERE\nRCQzzuhgu+2DBvZuP0pxWYCFl08cuBe2b98u2g88h73Sh9NWSunMv8Fmcx7z/lgixa/+exebjdex\nuQ1um75MoVZERERERGSIOWODbf3BTta9UYc3x8UXl9QMbOvTd3AvrbXPYhvhwUkZZZP/BsN27I/h\nYHMPv/jdDrrzPsBREmVe6flMGT72dJ+GiIiIiIiInMQZGWyTCZO3XtyLYRhcvqQGf8ANQPRILa37\nn8E2zI0zXUbZjFsxjMH72KbTFi+uP8wL7x7APmI3jpIGynwlLJl0RSZORURERERERE7ijAy2W9Yf\nIdwbZ/rcCkrL+6cO9x3aQ1vdr7EVO3Gmyiib9XUMwzbofbWNIVa/sZ+6xhD+MXWYRYcp8RZx54xv\n4ba7MnEqIiIiIiIichJnXLDt6Y7y4YYj5PhdzJzbv1hU786NdLb/HqPAgTNeRtl53xi092xtQ4jf\nvXuAnYe6ABg5pYl2Ty3F3kLunPEtAi5/Rs5FRERERERETu6MC7bvvVaLaVrMXTQWp8tB18b/pifx\nDkbQgSc9luLzbsQwDFJmml2HOnn1/Xp2fRRoJ1UGKK1qZmPndgo9Bdw541vkurVYlIiIiIiIyFB2\nRgXb2j2tHKrtoHxkLuOqSmh783n6vDswchzkOKeRf85i9h7pZsOuFj7Y20oklgJg7Bg7ZePa2Rd5\nh8OdEQo8+dw54zby3LkZPiMRERERERE5mTMq2L78wg4MA2ZP99P4u3/CHN4DNhtu9zz+2D2WN//l\nPbrDCXDG8RdEmVRjksxppinaQFM3+Bw+Fo2cz8UVFyrUioiIiIiIZIkzKtjGIu3Mrqoj0fMmRqUd\nKwUvdkTZYb2JlXoXo9JJwBcjZcQwgcMAUZiUP57zy2czpbga53G2/hEREREREZGh64xKcQvnvw+A\nGbXYezTKa4kgEcOJ3ZXC4TVJ0UfAncvIwFhG+IcxIlBOZXCkRmdFRERERESy2BkVbPvaLbZ1pHi9\ndzy28HDGDy9k0fQRTBlbiM1mYFnWoNWQRUREREREJPudUcF2ve0qxlSV8L+HBSkvysFmGxxiFWpF\nRERERETOPGdUsP1fy+bT1tab6TJERERERETkNLJlugARERERERGRv8SQHrH98Y9/zNatWzEMg/vu\nu48pU6ZkuiQREREREREZYoZssN24cSOHDx9m9erV1NXVcd9997F69epMlyUiIiIiIiJDzJCdirxu\n3TouueQSAMaOHUsoFCIcDme4KhERERERERlqhmywbW9vJz8/f+BxQUEBbW1tGaxIREREREREhqIh\nOxX5kyzL+kzHFRcHPudK5POk9st+asPspzbMfmrD7Kb2y35qw+ynNsw+QzbYlpSU0N7ePvC4tbWV\n4uLik75P2/1kr+LigNovy6kNs5/aMPupDbOb2i/7qQ2zn9pwaDvRfzoM2anI8+bN45VXXgFg586d\nlJSU4Pf7M1yViIiIiIiIDDVDdsR2xowZVFdXs2zZMgzDYOXKlZkuSURERERERIagIRtsAe6+++5M\nlyAiIiIiIiJD3JCdiiwiIiIiIiLyWSjYioiIiIiISFZTsBUREREREZGspmArIiIiIiIiWU3BVkRE\nRERERLKagq2IiIiIiIhkNQVbERERERERyWoKtiIiIiIiIpLVDMuyrEwXISIiIiIiIvLn0oitiIiI\niIiIZDUFWxEREREREclqCrYiIiIiIiKS1RRsRUREREREJKsp2IqIiIiIiEhWU7AVERERERGRrObI\ndAGnwo9//GO2bt2KYRjcd999TJkyJdMlyWfw4IMPsmnTJlKpFLfddhtvvPEGO3fuJC8vD4Bbb72V\nBQsWZLZIOaENGzbw3e9+l/HjxwMwYcIEvv71r7NixQpM06S4uJif/OQnuFyuDFcqJ/L888+zdu3a\ngcc7duygpqaGvr4+fD4fAPfccw81NTWZKlFOYN++fXz729/mlltu4aabbqK5ufm4fW/t2rX8+7//\nOzabjeuuu46lS5dmunT5yPHa8N577yWVSuFwOPjJT35CcXEx1dXVzJgxY+B9Tz31FHa7PYOVCxzb\nft/73veOew2jPjh0fbIN77jjDrq6ugDo7u5m2rRp3HbbbSxevHjg72B+fj4PP/xwJsuWT5H1wXbj\nxo0cPnyY1atXU1dXx3333cfq1aszXZacxPr169m/fz+rV6+mq6uLq6++mjlz5vD3f//3LFy4MNPl\nyWc0e/bsQb/g7733Xm688UYuv/xy/vEf/5E1a9Zw4403ZrBC+TRLly4duMjauHEjL730ErW1tTzw\nwANMmDAhw9XJifT19XH//fczd+7cgecefvjhY/rel7/8ZR599FHWrFmD0+nk2muv5dJLLx248JbM\nOV4bPvTQQ1x33XVcccUVPPvsszz55JOsWLECv9/Pr371qwxWK590vPYDjrmG6evrUx8cok70e/Rj\n995778Dfx9GjR6sPZomsn4q8bt06LrnkEgDGjh1LKBQiHA5nuCo5mXPPPZd//ud/BiAYDBKNRjFN\nM8NVyV9qw4YNXHzxxQAsXLiQdevWZbgi+aweffRRvv3tb2e6DPkMXC4Xjz/+OCUlJQPPHa/vbd26\nlcmTJxMIBPB4PMyYMYPNmzdnqmz5H47XhitXruSyyy4D+keFuru7M1WenMTx2u941AeHrk9rwwMH\nDtDb26sZoFko64Nte3s7+fn5A48LCgpoa2vLYEXyWdjt9oGpjmvWrOHCCy/EbrfzzDPPcPPNN7N8\n+XI6OzszXKWcTG1tLd/61re44YYbeO+994hGowNTjwsLC9UXs8S2bdsYNmwYxcXFQP//Wn/lK1/h\nBz/4AbFYLMPVySc5HA48Hs+g547X99rb2ykoKBg4Rn8fh47jtaHP58Nut2OaJr/+9a9ZvHgxAIlE\ngrvuuotly5bx5JNPZqJc+YTjtR9wzDWM+uDQdaI2BHj66ae56aabBh63t7dzxx13sGzZskG378jQ\nk/VTkT/JsqxMlyB/gtdee401a9bwxBNPsGPHDvLy8qiqquJf//Vf+dnPfsYPfvCDTJcoJzBq1Chu\nv/12Lr/8curr67n55psHjbqrL2aPNWvWcPXVVwNw8803M3HiRCoqKli5ciXPPvsst956a4YrlD/F\nifqe+uTQZ5omK1asYM6cOQNTJFesWMGVV16JYRjcdNNNzJo1i8mTJ2e4Uvmkq6666phrmOnTpw86\nRn1w6EskEmzatIlVq1YBkJeXx3e/+12uvPJKent7Wbp0KXPmzDnpaL1kRtaP2JaUlNDe3j7wuLW1\ndWDUQYa2d955h1/84hc8/vjjBAIB5s6dS1VVFQCLFi1i3759Ga5QPk1paSlXXHEFhmFQUVFBUVER\noVBoYISvpaVFv/izxIYNGwYuwC699FIqKioA9cNs4vP5jul7x/v7qD45tN17771UVlZy++23Dzx3\nww03kJOTg8/nY86cOeqTQ9TxrmHUB7PP+++/P2gKst/vZ8mSJTidTgoKCqipqeHAgQMZrFA+TdYH\n23nz5vHKK68AsHPnTkpKSvD7/RmuSk6mt7eXBx98kMcee2xgEYXvfOc71NfXA/0X2h+vtitD09q1\na/nlL38JQFtbGx0dHVxzzTUD/fHVV19l/vz5mSxRPoOWlhZycnJwuVxYlsUtt9xCT08PoH6YTc4/\n//xj+t7UqVPZvn07PT09RCIRNm/ezKxZszJcqZzI2rVrcTqd3HHHHQPPHThwgLvuugvLskilUmze\nvFl9cog63jWM+mD22b59O5MmTRp4vH79eh544AGgf8GpPXv2MHr06EyVJyeR9VORZ8yYQXV1NcuW\nLcMwDFauXJnpkuQzePHFF+nq6uLOO+8ceO6aa67hzjvvxOv14vP5Bn6RyNC0aNEi7r77bl5//XWS\nySSrVq2iqqqKe+65h9WrV1NeXs6Xv/zlTJcpJ9HW1jZwD5hhGFx33XXccssteL1eSktL+c53vpPh\nCuWTduzYwT/8wz/Q2NiIw+HglVde4ac//Snf+973BvU9p9PJXXfdxa233ophGPzd3/0dgUAg0+UL\nx2/Djo4O3G43X/3qV4H+BTFXrVpFWVkZ1157LTabjUWLFmlBmyHgeO130003HXMN4/F41AeHqOO1\n4SOPPEJbW9vArCWAWbNm8cILL3D99ddjmibf/OY3KS0tzWDl8mkMSxP+RUREREREJItl/VRkERER\nERERObsp2IqIiIiIiEhWU7AVERERERGRrKZgKyIiIiIiIllNwVZERERERESymoKtiIjIabR7927u\nv/9+amtr2blz5yn5zJaWFtatWwfAb3/7W55//vlT8rkiIiLZQtv9iIiIZMDPf/5zioqKWLp06V/8\nWWvXrqWuro7ly5efgspERESyjyPTBYiIiJxNNmzYwC233EJBQQF+vx+Px8OFF17IypUr6ezsJBwO\n87WvfY3FixfzyCOP0NDQQFNTE/fccw+xWIyf/vSnuFwuYrEYK1euJBgM8tBDD2FZFnl5eYTDYVKp\nFMuXL+ett97i0UcfxePx4PV6uf/++yktLWXRokXcfPPN/OEPf6ChoYEf/vCHzJ07N9M/GhERkT+b\ngq2IiMhpNm3aNCorK5k5cyaLFy/mhz/8IfPnz2fJkiX09fVx1VVXMW/ePAAaGhp45plnMAyD1157\njVWrVjFp0iR+//vf89hjj/Hwww9z9dVXk0ql+NrXvsYjjzwCQDQa5fvf/z5r1qyhrKyMZ555hoce\neogHHngAALfbzRNPPMF//ud/8vTTTyvYiohIVlOwFRERybANGzawfft2XnjhBQAcDgcNDQ0ATJ06\nFcMwACgqKuLBBx8kHo/T29tLbm7uCT/z0KFDFBYWUlZWBsDs2bN57rnnBl6fPXs2AOXl5YRCoc/l\nvERERE4XBVsREZEMc7lcrFy57YAKfAAAAVFJREFUksmTJw96/u2338bpdA48XrFixcC04TfffJMn\nnnjihJ/5cRj+mGVZg55zOByDXhMREclmWhVZREQkAwzDIJlMAjBz5kxeeuklAGKxGKtWrSKVSh3z\nnvb2dsaPH49pmrz88sskEomBz/rk8aNGjaKjo4OmpiYA1q1bx9SpUz/PUxIREckYjdiKiIhkwJw5\nc3jwwQexLIvbb7+d73//+9xwww0kEgmuv/76QSOqH/vGN77BX//1X1NeXs6tt97KihUreOqpp5g1\naxbLly/H6XRit9sB8Hg8/OhHP2L58uW4XC58Ph8/+tGPTvdpioiInBba7kdERERERESymqYii4iI\niIiISFZTsBUREREREZGspmArIiIiIiIiWU3BVkRERERERLKagq2IiIiIiIhkNQVbERERERERyWoK\ntiIiIiIiIpLVFGxFREREREQkq/0/UU366JWRdJgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "MPazOzyFaoze", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Example 2: Load the raw data for our sample experiments and plot using a different plotting package." + ] + }, + { + "metadata": { + "id": "0OCpL9IZOF08", + "colab_type": "code", + "outputId": "32279e50-34d5-44fc-dbe9-7b48c611162a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1094 + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "for game in GAMES:\n", + " # Use our provided colab utils to load this log file. The second returned \n", + " raw_data, _ = colab_utils.load_statistics(\n", + " '/content/samples/rainbow/{}_v4/logs'.format(game), verbose=False)\n", + " summarized_data = colab_utils.summarize_data(\n", + " raw_data, ['train_episode_returns'])\n", + " plt.plot(summarized_data['train_episode_returns'], label='episode returns')\n", + " plt.plot()\n", + " plt.title('Rainbow training - {}'.format(game))\n", + " plt.xlabel('Iteration')\n", + " plt.ylabel('Return')\n", + " plt.legend()\n", + " plt.show()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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p6N4XCCtFfAUWA0rypb3tDXo26Xa9cvGgyxeKmZ/XspqYJXvyA0mjOtMPCzSn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\nrRAEEaIoYvuBRvgCiV327K4AwryoZO+AlOkDscV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ZZ57J/PnzAdizZw9Tp07liiuu4JZbbqGmpiYSZYVlmKZmrIuIiG21eZBXVFTw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\nLiIiDqK0OoCpyW4iIuIgCvI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N/d/bWqLXyvH6NrkmZ8Jv+Kmc/367jw0/lLf4GcYtchqX00p5jYdGX5DcLizP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\n/wfNlu4XWbKqmQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} diff --git a/dopamine/colab/tensorboard.ipynb b/dopamine/colab/tensorboard.ipynb index 778e780..880bd99 100644 --- a/dopamine/colab/tensorboard.ipynb +++ b/dopamine/colab/tensorboard.ipynb @@ -1,112 +1,112 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "tensorboard.ipynb", - "version": "0.3.2", - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - } - }, - "cells": [ - { - "metadata": { - "id": "VYNA79KmgvbY", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Copyright 2018 The Dopamine Authors.\n", - "\n", - "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", - "\n", - "https://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." - ] - }, - { - "metadata": { - "id": "Ctd9k0h6wnqT", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# Visualize Dopamine baselines with Tensorboard\n", - "This colab allows you to easily view the trained baselines with Tensorboard (even if you don't have Tensorboard on your local machine!).\n", - "\n", - "Simply specify the game you would like to visualize and then run the cells in order.\n", - "\n", - "_The instructions for setting up Tensorboard were obtained from https://www.dlology.com/blog/quick-guide-to-run-tensorboard-in-google-colab/_" - ] - }, - { - "metadata": { - "id": "s8r_45_0qpmb", - "colab_type": "code", - "colab": {}, - "cellView": "form" - }, - "cell_type": "code", - "source": [ - "# @title Prepare all necessary files and binaries.\n", - "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n", - "!unzip ngrok-stable-linux-amd64.zip\n", - "!gsutil -q -m cp -R gs://download-dopamine-rl/compiled_tb_event_files.tar.gz /content/\n", - "!tar -xvzf /content/compiled_tb_event_files.tar.gz" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "D-oZRzeWwHZN", - "colab_type": "code", - "colab": {}, - "cellView": "form" - }, - "cell_type": "code", - "source": [ - "# @title Select which game to visualize.\n", - "game = 'Asterix' # @param['AirRaid', 'Alien', 'Amidar', 'Assault', 'Asterix', 'Asteroids', 'Atlantis', 'BankHeist', 'BattleZone', 'BeamRider', 'Berzerk', 'Bowling', 'Boxing', 'Breakout', 'Carnival', 'Centipede', 'ChopperCommand', 'CrazyClimber', 'DemonAttack', 'DoubleDunk', 'ElevatorAction', 'Enduro', 'FishingDerby', 'Freeway', 'Frostbite', 'Gopher', 'Gravitar', 'Hero', 'IceHockey', 'Jamesbond', 'JourneyEscape', 'Kangaroo', 'Krull', 'KungFuMaster', 'MontezumaRevenge', 'MsPacman', 'NameThisGame', 'Phoenix', 'Pitfall', 'Pong', 'Pooyan', 'PrivateEye', 'Qbert', 'Riverraid', 'RoadRunner', 'Robotank', 'Seaquest', 'Skiing', 'Solaris', 'SpaceInvaders', 'StarGunner', 'Tennis', 'TimePilot', 'Tutankham', 'UpNDown', 'Venture', 'VideoPinball', 'WizardOfWor', 'YarsRevenge', 'Zaxxon']\n", - "agents = ['dqn', 'c51', 'rainbow', 'iqn']\n", - "for agent in agents:\n", - " for run in range(1, 6):\n", - " !mkdir -p \"/content/$game/$agent/$run\"\n", - " !cp -r \"/content/$agent/$game/$run\" \"/content/$game/$agent/$run\"\n", - "LOG_DIR = '/content/{}'.format(game)\n", - "get_ipython().system_raw(\n", - " 'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n", - " .format(LOG_DIR)\n", - ")" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "zlKKnaP4y9FA", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "cellView": "form", - "outputId": "3abff714-c484-436e-dc5f-88b15511f4f2" - }, - "cell_type": "code", - "source": [ - "# @title Start the tensorboard\n", - "get_ipython().system_raw('./ngrok http 6006 &')\n", - "! curl -s http://localhost:4040/api/tunnels | python3 -c \\\n", - " \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"" - ], - "execution_count": 0, - "outputs": [] - } - ] -} +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "tensorboard.ipynb", + "version": "0.3.2", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "metadata": { + "id": "VYNA79KmgvbY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Copyright 2018 The Dopamine Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", + "\n", + "https://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + ] + }, + { + "metadata": { + "id": "Ctd9k0h6wnqT", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Visualize Dopamine baselines with Tensorboard\n", + "This colab allows you to easily view the trained baselines with Tensorboard (even if you don't have Tensorboard on your local machine!).\n", + "\n", + "Simply specify the game you would like to visualize and then run the cells in order.\n", + "\n", + "_The instructions for setting up Tensorboard were obtained from https://www.dlology.com/blog/quick-guide-to-run-tensorboard-in-google-colab/_" + ] + }, + { + "metadata": { + "id": "s8r_45_0qpmb", + "colab_type": "code", + "colab": {}, + "cellView": "form" + }, + "cell_type": "code", + "source": [ + "# @title Prepare all necessary files and binaries.\n", + "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n", + "!unzip ngrok-stable-linux-amd64.zip\n", + "!gsutil -q -m cp -R gs://download-dopamine-rl/compiled_tb_event_files.tar.gz /content/\n", + "!tar -xvzf /content/compiled_tb_event_files.tar.gz" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "D-oZRzeWwHZN", + "colab_type": "code", + "colab": {}, + "cellView": "form" + }, + "cell_type": "code", + "source": [ + "# @title Select which game to visualize.\n", + "game = 'Asterix' # @param['AirRaid', 'Alien', 'Amidar', 'Assault', 'Asterix', 'Asteroids', 'Atlantis', 'BankHeist', 'BattleZone', 'BeamRider', 'Berzerk', 'Bowling', 'Boxing', 'Breakout', 'Carnival', 'Centipede', 'ChopperCommand', 'CrazyClimber', 'DemonAttack', 'DoubleDunk', 'ElevatorAction', 'Enduro', 'FishingDerby', 'Freeway', 'Frostbite', 'Gopher', 'Gravitar', 'Hero', 'IceHockey', 'Jamesbond', 'JourneyEscape', 'Kangaroo', 'Krull', 'KungFuMaster', 'MontezumaRevenge', 'MsPacman', 'NameThisGame', 'Phoenix', 'Pitfall', 'Pong', 'Pooyan', 'PrivateEye', 'Qbert', 'Riverraid', 'RoadRunner', 'Robotank', 'Seaquest', 'Skiing', 'Solaris', 'SpaceInvaders', 'StarGunner', 'Tennis', 'TimePilot', 'Tutankham', 'UpNDown', 'Venture', 'VideoPinball', 'WizardOfWor', 'YarsRevenge', 'Zaxxon']\n", + "agents = ['dqn', 'c51', 'rainbow', 'iqn']\n", + "for agent in agents:\n", + " for run in range(1, 6):\n", + " !mkdir -p \"/content/$game/$agent/$run\"\n", + " !cp -r \"/content/$agent/$game/$run\" \"/content/$game/$agent/$run\"\n", + "LOG_DIR = '/content/{}'.format(game)\n", + "get_ipython().system_raw(\n", + " 'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n", + " .format(LOG_DIR)\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zlKKnaP4y9FA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "cellView": "form", + "outputId": "3abff714-c484-436e-dc5f-88b15511f4f2" + }, + "cell_type": "code", + "source": [ + "# @title Start the tensorboard\n", + "get_ipython().system_raw('./ngrok http 6006 &')\n", + "! curl -s http://localhost:4040/api/tunnels | python3 -c \\\n", + " \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"" + ], + "execution_count": 0, + "outputs": [] + } + ] +} diff --git a/dopamine/colab/utils.py b/dopamine/colab/utils.py index cb9f9f7..552537c 100644 --- a/dopamine/colab/utils.py +++ b/dopamine/colab/utils.py @@ -1,280 +1,280 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""This provides utilities for dealing with Dopamine data. - -See: dopamine/common/logger.py . -""" - -import itertools -import os -import pickle -import sys - - - -import numpy as np -import pandas as pd - -import tensorflow as tf - -FILE_PREFIX = 'log' -ITERATION_PREFIX = 'iteration_' - -ALL_GAMES = ['AirRaid', 'Alien', 'Amidar', 'Assault', 'Asterix', 'Asteroids', - 'Atlantis', 'BankHeist', 'BattleZone', 'BeamRider', 'Berzerk', - 'Bowling', 'Boxing', 'Breakout', 'Carnival', 'Centipede', - 'ChopperCommand', 'CrazyClimber', 'DemonAttack', 'DoubleDunk', - 'ElevatorAction', 'Enduro', 'FishingDerby', 'Freeway', 'Frostbite', - 'Gopher', 'Gravitar', 'Hero', 'IceHockey', 'Jamesbond', - 'JourneyEscape', 'Kangaroo', 'Krull', 'KungFuMaster', - 'MontezumaRevenge', 'MsPacman', 'NameThisGame', 'Phoenix', - 'Pitfall', 'Pong', 'Pooyan', 'PrivateEye', 'Qbert', 'Riverraid', - 'RoadRunner', 'Robotank', 'Seaquest', 'Skiing', 'Solaris', - 'SpaceInvaders', 'StarGunner', 'Tennis', 'TimePilot', 'Tutankham', - 'UpNDown', 'Venture', 'VideoPinball', 'WizardOfWor', 'YarsRevenge', - 'Zaxxon'] - - -def load_baselines(base_dir, verbose=False): - """Reads in the baseline experimental data from a specified base directory. - - Args: - base_dir: string, base directory where to read data from. - verbose: bool, whether to print warning messages. - - Returns: - A dict containing pandas DataFrames for all available agents and games. - """ - experimental_data = {} - for game in ALL_GAMES: - for agent in ['dqn', 'c51', 'rainbow', 'iqn']: - game_data_file = os.path.join(base_dir, agent, '{}.pkl'.format(game)) - if not tf.gfile.Exists(game_data_file): - if verbose: - # pylint: disable=superfluous-parens - print('Unable to load data for agent {} on game {}'.format(agent, - game)) - # pylint: enable=superfluous-parens - continue - with tf.gfile.Open(game_data_file, 'rb') as f: - if sys.version_info.major >= 3: - # pylint: disable=unexpected-keyword-arg - single_agent_data = pickle.load(f, encoding='latin1') - # pylint: enable=unexpected-keyword-arg - else: - single_agent_data = pickle.load(f) - single_agent_data['agent'] = agent - if game in experimental_data: - experimental_data[game] = experimental_data[game].merge( - single_agent_data, how='outer') - else: - experimental_data[game] = single_agent_data - return experimental_data - - -def load_statistics(log_path, iteration_number=None, verbose=True): - """Reads in a statistics object from log_path. - - Args: - log_path: string, provides the full path to the training/eval statistics. - iteration_number: The iteration number of the statistics object we want - to read. If set to None, load the latest version. - verbose: Whether to output information about the load procedure. - - Returns: - data: The requested statistics object. - iteration: The corresponding iteration number. - - Raises: - Exception: if data is not present. - """ - # If no iteration is specified, we'll look for the most recent. - if iteration_number is None: - iteration_number = get_latest_iteration(log_path) - - log_file = '%s/%s_%d' % (log_path, FILE_PREFIX, iteration_number) - - if verbose: - # pylint: disable=superfluous-parens - print('Reading statistics from: {}'.format(log_file)) - # pylint: enable=superfluous-parens - - with tf.gfile.Open(log_file, 'rb') as f: - return pickle.load(f), iteration_number - - -def get_latest_file(path): - """Return the file named 'path_[0-9]*' with the largest such number. - - Args: - path: The base path (including directory and base name) to search. - - Returns: - The latest file (in terms of given numbers). - """ - try: - latest_iteration = get_latest_iteration(path) - return os.path.join(path, '{}_{}'.format(FILE_PREFIX, latest_iteration)) - except ValueError: - return None - - -def get_latest_iteration(path): - """Return the largest iteration number corresponding to the given path. - - Args: - path: The base path (including directory and base name) to search. - - Returns: - The latest iteration number. - - Raises: - ValueError: if there is not available log data at the given path. - """ - glob = os.path.join(path, '{}_[0-9]*'.format(FILE_PREFIX)) - log_files = tf.gfile.Glob(glob) - - if not log_files: - raise ValueError('No log data found at {}'.format(path)) - - def extract_iteration(x): - return int(x[x.rfind('_') + 1:]) - - latest_iteration = max(extract_iteration(x) for x in log_files) - return latest_iteration - - -def summarize_data(data, summary_keys): - """Processes log data into a per-iteration summary. - - Args: - data: Dictionary loaded by load_statistics describing the data. This - dictionary has keys iteration_0, iteration_1, ... describing per-iteration - data. - summary_keys: List of per-iteration data to be summarized. - - Example: - data = load_statistics(...) - summarize_data(data, ['train_episode_returns', - 'eval_episode_returns']) - - Returns: - A dictionary mapping each key in returns_keys to a per-iteration summary. - """ - summary = {} - latest_iteration_number = len(data.keys()) - current_value = None - - for key in summary_keys: - summary[key] = [] - # Compute per-iteration average of the given key. - for i in range(latest_iteration_number): - iter_key = '{}{}'.format(ITERATION_PREFIX, i) - # We allow reporting the same value multiple times when data is missing. - # If there is no data for this iteration, use the previous'. - if iter_key in data: - current_value = np.mean(data[iter_key][key]) - summary[key].append(current_value) - - return summary - - -def read_experiment(log_path, - parameter_set=None, - job_descriptor='', - iteration_number=None, - summary_keys=('train_episode_returns', - 'eval_episode_returns'), - verbose=False): - """Reads in a set of experimental results from log_path. - - The provided parameter_set is an ordered_dict which - 1) defines the parameters of this experiment, - 2) defines the order in which they occur in the job descriptor. - - The method reads all experiments of the form - - ${log_path}/${job_descriptor}.format(params)/logs, - - where params is constructed from the cross product of the elements in - the parameter_set. - - For example: - parameter_set = collections.OrderedDict([ - ('game', ['Asterix', 'Pong']), - ('epsilon', ['0', '0.1']) - ]) - read_experiment('/tmp/logs', parameter_set, job_descriptor='{}_{}') - Will try to read logs from: - - /tmp/logs/Asterix_0/logs - - /tmp/logs/Asterix_0.1/logs - - /tmp/logs/Pong_0/logs - - /tmp/logs/Pong_0.1/logs - - Args: - log_path: string, base path specifying where results live. - parameter_set: An ordered_dict mapping parameter names to allowable values. - job_descriptor: A job descriptor string which is used to construct the full - path for each trial within an experiment. - iteration_number: Int, if not None determines the iteration number at which - we read in results. - summary_keys: Iterable of strings, iteration statistics to summarize. - verbose: If True, print out additional information. - - Returns: - A Pandas dataframe containing experimental results. - """ - keys = [] if parameter_set is None else list(parameter_set.keys()) - # Extract parameter value lists, one per parameter. - ordered_values = [parameter_set[key] for key in keys] - - column_names = keys + ['iteration'] + list(summary_keys) - num_parameter_settings = len([_ for _ in itertools.product(*ordered_values)]) - expected_num_iterations = 200 - expected_num_rows = num_parameter_settings * expected_num_iterations - - # Create DataFrame with predicted number of rows. - data_frame = pd.DataFrame(index=np.arange(0, expected_num_rows), - columns=column_names) - row_index = 0 - - # Now take their cross product. This generates tuples of the form - # (p1, p2, p3, ...) where p1, p2, p3 are parameter values for the first, - # second, etc. parameters as ordered in value_set. - for parameter_tuple in itertools.product(*ordered_values): - if job_descriptor is not None: - name = job_descriptor.format(*parameter_tuple) - else: - # Construct name for values. - name = '-'.join([keys[i] + '_' + str(parameter_tuple[i]) - for i in range(len(keys))]) - - experiment_path = '{}/{}/logs'.format(log_path, name) - - raw_data, last_iteration = load_statistics( - experiment_path, iteration_number=iteration_number, verbose=verbose) - - summary = summarize_data(raw_data, summary_keys) - for iteration in range(last_iteration): - # The row contains all the parameters, the iteration, and finally the - # requested values. - row_data = (list(parameter_tuple) + [iteration] + - [summary[key][iteration] for key in summary_keys]) - data_frame.loc[row_index] = row_data - - row_index += 1 - - # Shed any unused rows. - return data_frame.drop(np.arange(row_index, expected_num_rows)) +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""This provides utilities for dealing with Dopamine data. + +See: dopamine/common/logger.py . +""" + +import itertools +import os +import pickle +import sys + + + +import numpy as np +import pandas as pd + +import tensorflow as tf + +FILE_PREFIX = 'log' +ITERATION_PREFIX = 'iteration_' + +ALL_GAMES = ['AirRaid', 'Alien', 'Amidar', 'Assault', 'Asterix', 'Asteroids', + 'Atlantis', 'BankHeist', 'BattleZone', 'BeamRider', 'Berzerk', + 'Bowling', 'Boxing', 'Breakout', 'Carnival', 'Centipede', + 'ChopperCommand', 'CrazyClimber', 'DemonAttack', 'DoubleDunk', + 'ElevatorAction', 'Enduro', 'FishingDerby', 'Freeway', 'Frostbite', + 'Gopher', 'Gravitar', 'Hero', 'IceHockey', 'Jamesbond', + 'JourneyEscape', 'Kangaroo', 'Krull', 'KungFuMaster', + 'MontezumaRevenge', 'MsPacman', 'NameThisGame', 'Phoenix', + 'Pitfall', 'Pong', 'Pooyan', 'PrivateEye', 'Qbert', 'Riverraid', + 'RoadRunner', 'Robotank', 'Seaquest', 'Skiing', 'Solaris', + 'SpaceInvaders', 'StarGunner', 'Tennis', 'TimePilot', 'Tutankham', + 'UpNDown', 'Venture', 'VideoPinball', 'WizardOfWor', 'YarsRevenge', + 'Zaxxon'] + + +def load_baselines(base_dir, verbose=False): + """Reads in the baseline experimental data from a specified base directory. + + Args: + base_dir: string, base directory where to read data from. + verbose: bool, whether to print warning messages. + + Returns: + A dict containing pandas DataFrames for all available agents and games. + """ + experimental_data = {} + for game in ALL_GAMES: + for agent in ['dqn', 'c51', 'rainbow', 'iqn']: + game_data_file = os.path.join(base_dir, agent, '{}.pkl'.format(game)) + if not tf.gfile.Exists(game_data_file): + if verbose: + # pylint: disable=superfluous-parens + print('Unable to load data for agent {} on game {}'.format(agent, + game)) + # pylint: enable=superfluous-parens + continue + with tf.gfile.Open(game_data_file, 'rb') as f: + if sys.version_info.major >= 3: + # pylint: disable=unexpected-keyword-arg + single_agent_data = pickle.load(f, encoding='latin1') + # pylint: enable=unexpected-keyword-arg + else: + single_agent_data = pickle.load(f) + single_agent_data['agent'] = agent + if game in experimental_data: + experimental_data[game] = experimental_data[game].merge( + single_agent_data, how='outer') + else: + experimental_data[game] = single_agent_data + return experimental_data + + +def load_statistics(log_path, iteration_number=None, verbose=True): + """Reads in a statistics object from log_path. + + Args: + log_path: string, provides the full path to the training/eval statistics. + iteration_number: The iteration number of the statistics object we want + to read. If set to None, load the latest version. + verbose: Whether to output information about the load procedure. + + Returns: + data: The requested statistics object. + iteration: The corresponding iteration number. + + Raises: + Exception: if data is not present. + """ + # If no iteration is specified, we'll look for the most recent. + if iteration_number is None: + iteration_number = get_latest_iteration(log_path) + + log_file = '%s/%s_%d' % (log_path, FILE_PREFIX, iteration_number) + + if verbose: + # pylint: disable=superfluous-parens + print('Reading statistics from: {}'.format(log_file)) + # pylint: enable=superfluous-parens + + with tf.gfile.Open(log_file, 'rb') as f: + return pickle.load(f), iteration_number + + +def get_latest_file(path): + """Return the file named 'path_[0-9]*' with the largest such number. + + Args: + path: The base path (including directory and base name) to search. + + Returns: + The latest file (in terms of given numbers). + """ + try: + latest_iteration = get_latest_iteration(path) + return os.path.join(path, '{}_{}'.format(FILE_PREFIX, latest_iteration)) + except ValueError: + return None + + +def get_latest_iteration(path): + """Return the largest iteration number corresponding to the given path. + + Args: + path: The base path (including directory and base name) to search. + + Returns: + The latest iteration number. + + Raises: + ValueError: if there is not available log data at the given path. + """ + glob = os.path.join(path, '{}_[0-9]*'.format(FILE_PREFIX)) + log_files = tf.gfile.Glob(glob) + + if not log_files: + raise ValueError('No log data found at {}'.format(path)) + + def extract_iteration(x): + return int(x[x.rfind('_') + 1:]) + + latest_iteration = max(extract_iteration(x) for x in log_files) + return latest_iteration + + +def summarize_data(data, summary_keys): + """Processes log data into a per-iteration summary. + + Args: + data: Dictionary loaded by load_statistics describing the data. This + dictionary has keys iteration_0, iteration_1, ... describing per-iteration + data. + summary_keys: List of per-iteration data to be summarized. + + Example: + data = load_statistics(...) + summarize_data(data, ['train_episode_returns', + 'eval_episode_returns']) + + Returns: + A dictionary mapping each key in returns_keys to a per-iteration summary. + """ + summary = {} + latest_iteration_number = len(data.keys()) + current_value = None + + for key in summary_keys: + summary[key] = [] + # Compute per-iteration average of the given key. + for i in range(latest_iteration_number): + iter_key = '{}{}'.format(ITERATION_PREFIX, i) + # We allow reporting the same value multiple times when data is missing. + # If there is no data for this iteration, use the previous'. + if iter_key in data: + current_value = np.mean(data[iter_key][key]) + summary[key].append(current_value) + + return summary + + +def read_experiment(log_path, + parameter_set=None, + job_descriptor='', + iteration_number=None, + summary_keys=('train_episode_returns', + 'eval_episode_returns'), + verbose=False): + """Reads in a set of experimental results from log_path. + + The provided parameter_set is an ordered_dict which + 1) defines the parameters of this experiment, + 2) defines the order in which they occur in the job descriptor. + + The method reads all experiments of the form + + ${log_path}/${job_descriptor}.format(params)/logs, + + where params is constructed from the cross product of the elements in + the parameter_set. + + For example: + parameter_set = collections.OrderedDict([ + ('game', ['Asterix', 'Pong']), + ('epsilon', ['0', '0.1']) + ]) + read_experiment('/tmp/logs', parameter_set, job_descriptor='{}_{}') + Will try to read logs from: + - /tmp/logs/Asterix_0/logs + - /tmp/logs/Asterix_0.1/logs + - /tmp/logs/Pong_0/logs + - /tmp/logs/Pong_0.1/logs + + Args: + log_path: string, base path specifying where results live. + parameter_set: An ordered_dict mapping parameter names to allowable values. + job_descriptor: A job descriptor string which is used to construct the full + path for each trial within an experiment. + iteration_number: Int, if not None determines the iteration number at which + we read in results. + summary_keys: Iterable of strings, iteration statistics to summarize. + verbose: If True, print out additional information. + + Returns: + A Pandas dataframe containing experimental results. + """ + keys = [] if parameter_set is None else list(parameter_set.keys()) + # Extract parameter value lists, one per parameter. + ordered_values = [parameter_set[key] for key in keys] + + column_names = keys + ['iteration'] + list(summary_keys) + num_parameter_settings = len([_ for _ in itertools.product(*ordered_values)]) + expected_num_iterations = 200 + expected_num_rows = num_parameter_settings * expected_num_iterations + + # Create DataFrame with predicted number of rows. + data_frame = pd.DataFrame(index=np.arange(0, expected_num_rows), + columns=column_names) + row_index = 0 + + # Now take their cross product. This generates tuples of the form + # (p1, p2, p3, ...) where p1, p2, p3 are parameter values for the first, + # second, etc. parameters as ordered in value_set. + for parameter_tuple in itertools.product(*ordered_values): + if job_descriptor is not None: + name = job_descriptor.format(*parameter_tuple) + else: + # Construct name for values. + name = '-'.join([keys[i] + '_' + str(parameter_tuple[i]) + for i in range(len(keys))]) + + experiment_path = '{}/{}/logs'.format(log_path, name) + + raw_data, last_iteration = load_statistics( + experiment_path, iteration_number=iteration_number, verbose=verbose) + + summary = summarize_data(raw_data, summary_keys) + for iteration in range(last_iteration): + # The row contains all the parameters, the iteration, and finally the + # requested values. + row_data = (list(parameter_tuple) + [iteration] + + [summary[key][iteration] for key in summary_keys]) + data_frame.loc[row_index] = row_data + + row_index += 1 + + # Shed any unused rows. + return data_frame.drop(np.arange(row_index, expected_num_rows)) diff --git a/dopamine/discrete_domains/.DS_Store b/dopamine/discrete_domains/.DS_Store index 21b5721..2335c90 100644 Binary files a/dopamine/discrete_domains/.DS_Store and b/dopamine/discrete_domains/.DS_Store differ diff --git a/dopamine/discrete_domains/__init__.py b/dopamine/discrete_domains/__init__.py index 9bad579..df3e58e 100644 --- a/dopamine/discrete_domains/__init__.py +++ b/dopamine/discrete_domains/__init__.py @@ -1 +1 @@ -# coding=utf-8 +# coding=utf-8 diff --git a/dopamine/discrete_domains/atari_lib.py b/dopamine/discrete_domains/atari_lib.py index 3f97610..992b3d7 100644 --- a/dopamine/discrete_domains/atari_lib.py +++ b/dopamine/discrete_domains/atari_lib.py @@ -1,512 +1,512 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Atari-specific utilities including Atari-specific network architectures. - -This includes a class implementing minimal Atari 2600 preprocessing, which -is in charge of: - . Emitting a terminal signal when losing a life (optional). - . Frame skipping and color pooling. - . Resizing the image before it is provided to the agent. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import math - - -import atari_py -import gym -from gym.spaces.box import Box -import numpy as np -import tensorflow as tf - -import gin.tf -import cv2 - -slim = tf.contrib.slim - - -NATURE_DQN_OBSERVATION_SHAPE = (84, 84) # Size of downscaled Atari 2600 frame. -NATURE_DQN_DTYPE = tf.uint8 # DType of Atari 2600 observations. -NATURE_DQN_STACK_SIZE = 4 # Number of frames in the state stack. - - - - -@gin.configurable -def create_atari_environment(game_name=None, sticky_actions=True): - """Wraps an Atari 2600 Gym environment with some basic preprocessing. - - This preprocessing matches the guidelines proposed in Machado et al. (2017), - "Revisiting the Arcade Learning Environment: Evaluation Protocols and Open - Problems for General Agents". - - The created environment is the Gym wrapper around the Arcade Learning - Environment. - - The main choice available to the user is whether to use sticky actions or not. - Sticky actions, as prescribed by Machado et al., cause actions to persist - with some probability (0.25) when a new command is sent to the ALE. This - can be viewed as introducing a mild form of stochasticity in the environment. - We use them by default. - - Args: - game_name: str, the name of the Atari 2600 domain. - sticky_actions: bool, whether to use sticky_actions as per Machado et al. - - Returns: - An Atari 2600 environment with some standard preprocessing. - """ - print ('STICKY_ACTIONS:', sticky_actions) - assert game_name is not None - game_version = 'v0' if sticky_actions else 'v4' - full_game_name = '{}NoFrameskip-{}'.format(game_name, game_version) - env = gym.make(full_game_name) - # Strip out the TimeLimit wrapper from Gym, which caps us at 100k frames. We - # handle this time limit internally instead, which lets us cap at 108k frames - # (30 minutes). The TimeLimit wrapper also plays poorly with saving and - # restoring states. - env = env.env - env = AtariPreprocessing(env) - return env - - -def nature_dqn_network(num_actions, network_type, state, aux=False, next_state=None): - """The convolutional network used to compute the agent's Q-values. - - Args: - num_actions: int, number of actions. - network_type: namedtuple, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - net = tf.cast(state, tf.float32) - net = tf.div(net, 255.) - net = slim.conv2d(net, 32, [8, 8], stride=4, scope='conv2d_1') - net = slim.conv2d(net, 64, [4, 4], stride=2, scope='conv2d_2') - net = slim.conv2d(net, 64, [3, 3], stride=1, scope='conv2d_3') - net = slim.flatten(net) - - net = slim.fully_connected(net, 512) - q_values = slim.fully_connected(net, num_actions, activation_fn=None) - return network_type(q_values, None) - -#@profile -def rainbow_network(num_actions, num_atoms, num_atoms_sub, support, network_type, state, runtype='run', v_support=None, a_support=None, big_z=None, big_a=None, big_qv=None, N=1, index=None, M=None, sp_a=None, unique_num=None, sortsp_a=None, v_sup_tensor=None): #run, conv, convmean - """The convolutional network used to compute agent's Q-value distributions. - - Args: - num_actions: int, number of actions. - num_atoms: int, the number of buckets of the value function distribution. - support: tf.linspace, the support of the Q-value distribution. - network_type: namedtuple, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - weights_initializer = slim.variance_scaling_initializer( - factor=1.0 / np.sqrt(3.0), mode='FAN_IN', uniform=True) - - net = tf.cast(state, tf.float32) - net = tf.div(net, 255.) - state_input = net - net = slim.conv2d( - net, 32, [8, 8], stride=4, weights_initializer=weights_initializer) - net = slim.conv2d( - net, 64, [4, 4], stride=2, weights_initializer=weights_initializer) - net = slim.conv2d( - net, 64, [3, 3], stride=1, weights_initializer=weights_initializer) - feature = slim.flatten(net) - feature_size = int(feature.shape[-1]) - a_origin, Ea = None, None - net = slim.fully_connected(feature, 510, weights_initializer=weights_initializer) - net = slim.fully_connected( - net, - num_actions * num_atoms, - activation_fn=None, - weights_initializer=weights_initializer) - logits = tf.reshape(net, [-1, num_actions, num_atoms]) - probabilities = tf.contrib.layers.softmax(logits) - q_values = tf.reduce_sum(support * probabilities, axis=2) - return network_type(q_values, logits, probabilities, None, None, None, a_origin, Ea, None, None, None) - -#@profile -def implicit_quantile_network(num_actions, quantile_embedding_dim, - network_type, state, num_quantiles): - """The Implicit Quantile ConvNet. - - Args: - num_actions: int, number of actions. - quantile_embedding_dim: int, embedding dimension for the quantile input. - network_type: namedtuple, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - num_quantiles: int, number of quantile inputs. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - weights_initializer = slim.variance_scaling_initializer( - factor=1.0 / np.sqrt(3.0), mode='FAN_IN', uniform=True) - - state_net = tf.cast(state, tf.float32) - state_net = tf.div(state_net, 255.) - state_net = slim.conv2d( - state_net, 32, [8, 8], stride=4, - weights_initializer=weights_initializer) - state_net = slim.conv2d( - state_net, 64, [4, 4], stride=2, - weights_initializer=weights_initializer) - state_net = slim.conv2d( - state_net, 64, [3, 3], stride=1, - weights_initializer=weights_initializer) - state_net = slim.flatten(state_net) - print ('state_net:', state_net.shape, ", num_quan:", num_quantiles) - state_net_size = state_net.get_shape().as_list()[-1] - - state_net_tiled = tf.tile(state_net, [num_quantiles, 1]) - batch_size = state_net.get_shape().as_list()[0] - quantiles_shape = [batch_size * num_quantiles, 1] - quantiles = tf.random_uniform( - quantiles_shape, minval=0, maxval=1, dtype=tf.float32) - - quantile_net = tf.tile(quantiles, [1, quantile_embedding_dim]) - pi = tf.constant(math.pi) - quantile_net = tf.cast(tf.range( - 1, quantile_embedding_dim + 1, 1), tf.float32) * pi * quantile_net - quantile_net = tf.cos(quantile_net) - quantile_net = slim.fully_connected(quantile_net, state_net_size, - weights_initializer=weights_initializer) - net = tf.multiply(state_net_tiled, quantile_net) - net = slim.fully_connected( - net, 512, weights_initializer=weights_initializer) - quantile_values = slim.fully_connected( - net, - num_actions, - activation_fn=None, - weights_initializer=weights_initializer) - return network_type(quantile_values=quantile_values, quantiles=quantiles) - -def fqf_network(num_actions, quantile_embedding_dim, - network_type, state, num_quantiles, runtype='fqf'): - """The FQF ConvNet. - - Args: - num_actions: int, number of actions. - quantile_embedding_dim: int, embedding dimension for the quantile input. - network_type: namedtuple, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - num_quantiles: int, number of quantile inputs. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - weights_initializer = slim.variance_scaling_initializer( - factor=1.0 / np.sqrt(3.0), mode='FAN_IN', uniform=True) - - state_net = tf.cast(state, tf.float32) - state_net = tf.div(state_net, 255.) - state_net = slim.conv2d( - state_net, 32, [8, 8], stride=4, - weights_initializer=weights_initializer) - state_net = slim.conv2d( - state_net, 64, [4, 4], stride=2, - weights_initializer=weights_initializer) - state_net = slim.conv2d( - state_net, 64, [3, 3], stride=1, - weights_initializer=weights_initializer) - state_net = slim.flatten(state_net) - print ('state_net:', state_net.shape, ", num_quan:", num_quantiles) - state_net_size = state_net.get_shape().as_list()[-1] - - quantile_values_origin = None - quantiles_origin = None - Fv_diff = None - v_diff = None - L_tau = None - quantile_values_mid = None - quantiles_mid = None - gradient_tau = None - quantile_tau = None - - batch_size = state_net.get_shape().as_list()[0] - state_net1 = state_net - - quantiles_right = slim.fully_connected(state_net1, num_quantiles, weights_initializer=weights_initializer, scope='fqf', reuse=False, activation_fn=None) - quantiles_right = tf.reshape(quantiles_right, [batch_size, num_quantiles]) - quantiles_right = tf.contrib.layers.softmax(quantiles_right) * (1 - 0.00) - zeros = tf.zeros([batch_size, 1]) - quantiles_right = tf.cumsum(quantiles_right, axis=1) #batchsize x 32 - quantiles_all = tf.concat([zeros, quantiles_right], axis=-1) #33 - quantiles_left = quantiles_all[:, :-1] #32 - quantiles_center = quantiles_all[:, 1:-1] #31, delete 0&1 - quantiles = quantiles_center - quantiles_mid = (quantiles_right + quantiles_left) / 2 #batchsize x 32 - v_diff = quantiles_right - quantiles_left #32 - v_diff = tf.transpose(v_diff, [1, 0]) #quan x batchsize - - quantile_tau = quantiles - quantile_tau = tf.transpose(quantile_tau, [1, 0]) #quan x batchsize - quantile_tau = tf.reshape(quantile_tau, [num_quantiles-1, batch_size]) - - quantiles = tf.transpose(quantiles, [1, 0]) #quan-1 x batchsize - quantiles = tf.reshape(quantiles, [(num_quantiles-1) * batch_size , 1]) - quantile_net = tf.tile(quantiles, [1, quantile_embedding_dim]) - pi = tf.constant(math.pi) - quantile_net = tf.cast(tf.range( - 1, quantile_embedding_dim + 1, 1), tf.float32) * pi * quantile_net - quantile_net = tf.cos(quantile_net) - quantile_net = slim.fully_connected(quantile_net, state_net_size, - weights_initializer=weights_initializer, scope='quantile_net') - - quantiles_mid = tf.transpose(quantiles_mid, [1, 0]) #quan x batchsize - quantiles_mid = tf.reshape(quantiles_mid, [(num_quantiles)*batch_size, 1]) - quantile_net_mid = tf.tile(quantiles_mid, [1, quantile_embedding_dim]) - pi = tf.constant(math.pi) - quantile_net_mid = tf.cast(tf.range( - 1, quantile_embedding_dim + 1, 1), tf.float32) * pi * quantile_net_mid - quantile_net_mid = tf.cos(quantile_net_mid) - quantile_net_mid = slim.fully_connected(quantile_net_mid, state_net_size, - weights_initializer=weights_initializer, scope='quantile_net', reuse=True) - # Hadamard product. - state_net_tiled = tf.tile(state_net, [num_quantiles - 1, 1]) - net = tf.multiply(state_net_tiled, quantile_net) - net = slim.fully_connected( - net, 512, weights_initializer=weights_initializer) - quantile_values = slim.fully_connected( - net, - num_actions, - activation_fn=None, - weights_initializer=weights_initializer, - scope='quantile_values_net') - - state_net_tiled1 = tf.tile(state_net, [num_quantiles, 1]) - net1 = tf.multiply(state_net_tiled1, quantile_net_mid) - net1 = slim.fully_connected( - net1, 512, weights_initializer=weights_initializer) - quantile_values_mid = slim.fully_connected( - net1, - num_actions, - activation_fn=None, - weights_initializer=weights_initializer, - scope='quantile_values_net', reuse=True) - - quantile_values = tf.reshape(quantile_values, [num_quantiles-1, batch_size, num_actions]) - quantile_values_mid = tf.reshape(quantile_values_mid, [num_quantiles, batch_size, num_actions]) - quantile_values_mid_1 = quantile_values_mid[:-1, :, :] - quantile_values_mid_2 = quantile_values_mid[1:, :, :] - sum_1 = 2 * quantile_values #31 - sum_2 = quantile_values_mid_2 + quantile_values_mid_1 #31 - L_tau = tf.square(sum_1 - sum_2) #31 x batchsize x action - gradient_tau = sum_1 - sum_2 - print ("sum_1:", sum_1.shape) - print ("L_tau:", L_tau.shape) - quantile_values_mid = tf.reshape(quantile_values_mid, [-1, num_actions]) #32 x batchsize x action - quantile_values_mid = tf.reshape(quantile_values_mid, [-1, num_actions]) #32 x batchsize x action - - quantiles_mid = tf.reshape(quantiles_mid, [-1, 1]) #32 x batchsize x action - #quantile_values = quantile_values_mid - #quantile = quantile_mid - return network_type(quantile_values=quantile_values_mid, quantiles=quantiles_mid, quantile_values_origin=quantile_values_origin, quantiles_origin=quantiles_origin, Fv_diff=Fv_diff, v_diff=v_diff, quantile_values_mid=quantile_values_mid, quantiles_mid=quantiles_mid, L_tau=L_tau, gradient_tau=gradient_tau, quantile_tau=quantile_tau) - - -@gin.configurable -class AtariPreprocessing(object): - """A class implementing image preprocessing for Atari 2600 agents. - - Specifically, this provides the following subset from the JAIR paper - (Bellemare et al., 2013) and Nature DQN paper (Mnih et al., 2015): - - * Frame skipping (defaults to 4). - * Terminal signal when a life is lost (off by default). - * Grayscale and max-pooling of the last two frames. - * Downsample the screen to a square image (defaults to 84x84). - - More generally, this class follows the preprocessing guidelines set down in - Machado et al. (2018), "Revisiting the Arcade Learning Environment: - Evaluation Protocols and Open Problems for General Agents". - """ - - def __init__(self, environment, frame_skip=4, terminal_on_life_loss=False, - screen_size=84): - """Constructor for an Atari 2600 preprocessor. - - Args: - environment: Gym environment whose observations are preprocessed. - frame_skip: int, the frequency at which the agent experiences the game. - terminal_on_life_loss: bool, If True, the step() method returns - is_terminal=True whenever a life is lost. See Mnih et al. 2015. - screen_size: int, size of a resized Atari 2600 frame. - - Raises: - ValueError: if frame_skip or screen_size are not strictly positive. - """ - if frame_skip <= 0: - raise ValueError('Frame skip should be strictly positive, got {}'. - format(frame_skip)) - if screen_size <= 0: - raise ValueError('Target screen size should be strictly positive, got {}'. - format(screen_size)) - - self.environment = environment - self.terminal_on_life_loss = terminal_on_life_loss - self.frame_skip = frame_skip - self.screen_size = screen_size - - obs_dims = self.environment.observation_space - # Stores temporary observations used for pooling over two successive - # frames. - self.screen_buffer = [ - np.empty((obs_dims.shape[0], obs_dims.shape[1]), dtype=np.uint8), - np.empty((obs_dims.shape[0], obs_dims.shape[1]), dtype=np.uint8) - ] - - self.game_over = False - self.lives = 0 # Will need to be set by reset(). - - @property - def observation_space(self): - # Return the observation space adjusted to match the shape of the processed - # observations. - return Box(low=0, high=255, shape=(self.screen_size, self.screen_size, 1), - dtype=np.uint8) - - @property - def action_space(self): - return self.environment.action_space - - @property - def reward_range(self): - return self.environment.reward_range - - @property - def metadata(self): - return self.environment.metadata - - def reset(self): - """Resets the environment. - - Returns: - observation: numpy array, the initial observation emitted by the - environment. - """ - self.environment.reset() - self.lives = self.environment.ale.lives() - self._fetch_grayscale_observation(self.screen_buffer[0]) - self.screen_buffer[1].fill(0) - return self._pool_and_resize() - - def render(self, mode): - """Renders the current screen, before preprocessing. - - This calls the Gym API's render() method. - - Args: - mode: Mode argument for the environment's render() method. - Valid values (str) are: - 'rgb_array': returns the raw ALE image. - 'human': renders to display via the Gym renderer. - - Returns: - if mode='rgb_array': numpy array, the most recent screen. - if mode='human': bool, whether the rendering was successful. - """ - return self.environment.render(mode) - - def step(self, action): - """Applies the given action in the environment. - - Remarks: - - * If a terminal state (from life loss or episode end) is reached, this may - execute fewer than self.frame_skip steps in the environment. - * Furthermore, in this case the returned observation may not contain valid - image data and should be ignored. - - Args: - action: The action to be executed. - - Returns: - observation: numpy array, the observation following the action. - reward: float, the reward following the action. - is_terminal: bool, whether the environment has reached a terminal state. - This is true when a life is lost and terminal_on_life_loss, or when the - episode is over. - info: Gym API's info data structure. - """ - accumulated_reward = 0. - - for time_step in range(self.frame_skip): - # We bypass the Gym observation altogether and directly fetch the - # grayscale image from the ALE. This is a little faster. - _, reward, game_over, info = self.environment.step(action) - accumulated_reward += reward - - if self.terminal_on_life_loss: - new_lives = self.environment.ale.lives() - is_terminal = game_over or new_lives < self.lives - self.lives = new_lives - else: - is_terminal = game_over - - if is_terminal: - break - # We max-pool over the last two frames, in grayscale. - elif time_step >= self.frame_skip - 2: - t = time_step - (self.frame_skip - 2) - self._fetch_grayscale_observation(self.screen_buffer[t]) - - # Pool the last two observations. - observation = self._pool_and_resize() - - self.game_over = game_over - return observation, accumulated_reward, is_terminal, info - - def _fetch_grayscale_observation(self, output): - """Returns the current observation in grayscale. - - The returned observation is stored in 'output'. - - Args: - output: numpy array, screen buffer to hold the returned observation. - - Returns: - observation: numpy array, the current observation in grayscale. - """ - self.environment.ale.getScreenGrayscale(output) - return output - - def _pool_and_resize(self): - """Transforms two frames into a Nature DQN observation. - - For efficiency, the transformation is done in-place in self.screen_buffer. - - Returns: - transformed_screen: numpy array, pooled, resized screen. - """ - # Pool if there are enough screens to do so. - if self.frame_skip > 1: - np.maximum(self.screen_buffer[0], self.screen_buffer[1], - out=self.screen_buffer[0]) - - transformed_image = cv2.resize(self.screen_buffer[0], - (self.screen_size, self.screen_size), - interpolation=cv2.INTER_AREA) - int_image = np.asarray(transformed_image, dtype=np.uint8) - return np.expand_dims(int_image, axis=2) +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Atari-specific utilities including Atari-specific network architectures. + +This includes a class implementing minimal Atari 2600 preprocessing, which +is in charge of: + . Emitting a terminal signal when losing a life (optional). + . Frame skipping and color pooling. + . Resizing the image before it is provided to the agent. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + + +import atari_py +import gym +from gym.spaces.box import Box +import numpy as np +import tensorflow as tf + +import gin.tf +import cv2 + +slim = tf.contrib.slim + + +NATURE_DQN_OBSERVATION_SHAPE = (84, 84) # Size of downscaled Atari 2600 frame. +NATURE_DQN_DTYPE = tf.uint8 # DType of Atari 2600 observations. +NATURE_DQN_STACK_SIZE = 4 # Number of frames in the state stack. + + + + +@gin.configurable +def create_atari_environment(game_name=None, sticky_actions=True): + """Wraps an Atari 2600 Gym environment with some basic preprocessing. + + This preprocessing matches the guidelines proposed in Machado et al. (2017), + "Revisiting the Arcade Learning Environment: Evaluation Protocols and Open + Problems for General Agents". + + The created environment is the Gym wrapper around the Arcade Learning + Environment. + + The main choice available to the user is whether to use sticky actions or not. + Sticky actions, as prescribed by Machado et al., cause actions to persist + with some probability (0.25) when a new command is sent to the ALE. This + can be viewed as introducing a mild form of stochasticity in the environment. + We use them by default. + + Args: + game_name: str, the name of the Atari 2600 domain. + sticky_actions: bool, whether to use sticky_actions as per Machado et al. + + Returns: + An Atari 2600 environment with some standard preprocessing. + """ + print ('STICKY_ACTIONS:', sticky_actions) + assert game_name is not None + game_version = 'v0' if sticky_actions else 'v4' + full_game_name = '{}NoFrameskip-{}'.format(game_name, game_version) + env = gym.make(full_game_name) + # Strip out the TimeLimit wrapper from Gym, which caps us at 100k frames. We + # handle this time limit internally instead, which lets us cap at 108k frames + # (30 minutes). The TimeLimit wrapper also plays poorly with saving and + # restoring states. + env = env.env + env = AtariPreprocessing(env) + return env + + +def nature_dqn_network(num_actions, network_type, state, aux=False, next_state=None): + """The convolutional network used to compute the agent's Q-values. + + Args: + num_actions: int, number of actions. + network_type: namedtuple, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + net = tf.cast(state, tf.float32) + net = tf.div(net, 255.) + net = slim.conv2d(net, 32, [8, 8], stride=4, scope='conv2d_1') + net = slim.conv2d(net, 64, [4, 4], stride=2, scope='conv2d_2') + net = slim.conv2d(net, 64, [3, 3], stride=1, scope='conv2d_3') + net = slim.flatten(net) + + net = slim.fully_connected(net, 512) + q_values = slim.fully_connected(net, num_actions, activation_fn=None) + return network_type(q_values, None) + +#@profile +def rainbow_network(num_actions, num_atoms, num_atoms_sub, support, network_type, state, runtype='run', v_support=None, a_support=None, big_z=None, big_a=None, big_qv=None, N=1, index=None, M=None, sp_a=None, unique_num=None, sortsp_a=None, v_sup_tensor=None): #run, conv, convmean + """The convolutional network used to compute agent's Q-value distributions. + + Args: + num_actions: int, number of actions. + num_atoms: int, the number of buckets of the value function distribution. + support: tf.linspace, the support of the Q-value distribution. + network_type: namedtuple, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + weights_initializer = slim.variance_scaling_initializer( + factor=1.0 / np.sqrt(3.0), mode='FAN_IN', uniform=True) + + net = tf.cast(state, tf.float32) + net = tf.div(net, 255.) + state_input = net + net = slim.conv2d( + net, 32, [8, 8], stride=4, weights_initializer=weights_initializer) + net = slim.conv2d( + net, 64, [4, 4], stride=2, weights_initializer=weights_initializer) + net = slim.conv2d( + net, 64, [3, 3], stride=1, weights_initializer=weights_initializer) + feature = slim.flatten(net) + feature_size = int(feature.shape[-1]) + a_origin, Ea = None, None + net = slim.fully_connected(feature, 510, weights_initializer=weights_initializer) + net = slim.fully_connected( + net, + num_actions * num_atoms, + activation_fn=None, + weights_initializer=weights_initializer) + logits = tf.reshape(net, [-1, num_actions, num_atoms]) + probabilities = tf.contrib.layers.softmax(logits) + q_values = tf.reduce_sum(support * probabilities, axis=2) + return network_type(q_values, logits, probabilities, None, None, None, a_origin, Ea, None, None, None) + +#@profile +def implicit_quantile_network(num_actions, quantile_embedding_dim, + network_type, state, num_quantiles): + """The Implicit Quantile ConvNet. + + Args: + num_actions: int, number of actions. + quantile_embedding_dim: int, embedding dimension for the quantile input. + network_type: namedtuple, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + num_quantiles: int, number of quantile inputs. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + weights_initializer = slim.variance_scaling_initializer( + factor=1.0 / np.sqrt(3.0), mode='FAN_IN', uniform=True) + + state_net = tf.cast(state, tf.float32) + state_net = tf.div(state_net, 255.) + state_net = slim.conv2d( + state_net, 32, [8, 8], stride=4, + weights_initializer=weights_initializer) + state_net = slim.conv2d( + state_net, 64, [4, 4], stride=2, + weights_initializer=weights_initializer) + state_net = slim.conv2d( + state_net, 64, [3, 3], stride=1, + weights_initializer=weights_initializer) + state_net = slim.flatten(state_net) + print ('state_net:', state_net.shape, ", num_quan:", num_quantiles) + state_net_size = state_net.get_shape().as_list()[-1] + + state_net_tiled = tf.tile(state_net, [num_quantiles, 1]) + batch_size = state_net.get_shape().as_list()[0] + quantiles_shape = [batch_size * num_quantiles, 1] + quantiles = tf.random_uniform( + quantiles_shape, minval=0, maxval=1, dtype=tf.float32) + + quantile_net = tf.tile(quantiles, [1, quantile_embedding_dim]) + pi = tf.constant(math.pi) + quantile_net = tf.cast(tf.range( + 1, quantile_embedding_dim + 1, 1), tf.float32) * pi * quantile_net + quantile_net = tf.cos(quantile_net) + quantile_net = slim.fully_connected(quantile_net, state_net_size, + weights_initializer=weights_initializer) + net = tf.multiply(state_net_tiled, quantile_net) + net = slim.fully_connected( + net, 512, weights_initializer=weights_initializer) + quantile_values = slim.fully_connected( + net, + num_actions, + activation_fn=None, + weights_initializer=weights_initializer) + return network_type(quantile_values=quantile_values, quantiles=quantiles) + +def fqf_network(num_actions, quantile_embedding_dim, + network_type, state, num_quantiles, runtype='fqf'): + """The FQF ConvNet. + + Args: + num_actions: int, number of actions. + quantile_embedding_dim: int, embedding dimension for the quantile input. + network_type: namedtuple, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + num_quantiles: int, number of quantile inputs. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + weights_initializer = slim.variance_scaling_initializer( + factor=1.0 / np.sqrt(3.0), mode='FAN_IN', uniform=True) + + state_net = tf.cast(state, tf.float32) + state_net = tf.div(state_net, 255.) + state_net = slim.conv2d( + state_net, 32, [8, 8], stride=4, + weights_initializer=weights_initializer) + state_net = slim.conv2d( + state_net, 64, [4, 4], stride=2, + weights_initializer=weights_initializer) + state_net = slim.conv2d( + state_net, 64, [3, 3], stride=1, + weights_initializer=weights_initializer) + state_net = slim.flatten(state_net) + print ('state_net:', state_net.shape, ", num_quan:", num_quantiles) + state_net_size = state_net.get_shape().as_list()[-1] + + quantile_values_origin = None + quantiles_origin = None + Fv_diff = None + v_diff = None + L_tau = None + quantile_values_mid = None + quantiles_mid = None + gradient_tau = None + quantile_tau = None + + batch_size = state_net.get_shape().as_list()[0] + state_net1 = state_net + + quantiles_right = slim.fully_connected(state_net1, num_quantiles, weights_initializer=weights_initializer, scope='fqf', reuse=False, activation_fn=None) + quantiles_right = tf.reshape(quantiles_right, [batch_size, num_quantiles]) + quantiles_right = tf.contrib.layers.softmax(quantiles_right) * (1 - 0.00) + zeros = tf.zeros([batch_size, 1]) + quantiles_right = tf.cumsum(quantiles_right, axis=1) #batchsize x 32 + quantiles_all = tf.concat([zeros, quantiles_right], axis=-1) #33 + quantiles_left = quantiles_all[:, :-1] #32 + quantiles_center = quantiles_all[:, 1:-1] #31, delete 0&1 + quantiles = quantiles_center + quantiles_mid = (quantiles_right + quantiles_left) / 2 #batchsize x 32 + v_diff = quantiles_right - quantiles_left #32 + v_diff = tf.transpose(v_diff, [1, 0]) #quan x batchsize + + quantile_tau = quantiles + quantile_tau = tf.transpose(quantile_tau, [1, 0]) #quan x batchsize + quantile_tau = tf.reshape(quantile_tau, [num_quantiles-1, batch_size]) + + quantiles = tf.transpose(quantiles, [1, 0]) #quan-1 x batchsize + quantiles = tf.reshape(quantiles, [(num_quantiles-1) * batch_size , 1]) + quantile_net = tf.tile(quantiles, [1, quantile_embedding_dim]) + pi = tf.constant(math.pi) + quantile_net = tf.cast(tf.range( + 1, quantile_embedding_dim + 1, 1), tf.float32) * pi * quantile_net + quantile_net = tf.cos(quantile_net) + quantile_net = slim.fully_connected(quantile_net, state_net_size, + weights_initializer=weights_initializer, scope='quantile_net') + + quantiles_mid = tf.transpose(quantiles_mid, [1, 0]) #quan x batchsize + quantiles_mid = tf.reshape(quantiles_mid, [(num_quantiles)*batch_size, 1]) + quantile_net_mid = tf.tile(quantiles_mid, [1, quantile_embedding_dim]) + pi = tf.constant(math.pi) + quantile_net_mid = tf.cast(tf.range( + 1, quantile_embedding_dim + 1, 1), tf.float32) * pi * quantile_net_mid + quantile_net_mid = tf.cos(quantile_net_mid) + quantile_net_mid = slim.fully_connected(quantile_net_mid, state_net_size, + weights_initializer=weights_initializer, scope='quantile_net', reuse=True) + # Hadamard product. + state_net_tiled = tf.tile(state_net, [num_quantiles - 1, 1]) + net = tf.multiply(state_net_tiled, quantile_net) + net = slim.fully_connected( + net, 512, weights_initializer=weights_initializer) + quantile_values = slim.fully_connected( + net, + num_actions, + activation_fn=None, + weights_initializer=weights_initializer, + scope='quantile_values_net') + + state_net_tiled1 = tf.tile(state_net, [num_quantiles, 1]) + net1 = tf.multiply(state_net_tiled1, quantile_net_mid) + net1 = slim.fully_connected( + net1, 512, weights_initializer=weights_initializer) + quantile_values_mid = slim.fully_connected( + net1, + num_actions, + activation_fn=None, + weights_initializer=weights_initializer, + scope='quantile_values_net', reuse=True) + + quantile_values = tf.reshape(quantile_values, [num_quantiles-1, batch_size, num_actions]) + quantile_values_mid = tf.reshape(quantile_values_mid, [num_quantiles, batch_size, num_actions]) + quantile_values_mid_1 = quantile_values_mid[:-1, :, :] + quantile_values_mid_2 = quantile_values_mid[1:, :, :] + sum_1 = 2 * quantile_values #31 + sum_2 = quantile_values_mid_2 + quantile_values_mid_1 #31 + L_tau = tf.square(sum_1 - sum_2) #31 x batchsize x action + gradient_tau = sum_1 - sum_2 + print ("sum_1:", sum_1.shape) + print ("L_tau:", L_tau.shape) + quantile_values_mid = tf.reshape(quantile_values_mid, [-1, num_actions]) #32 x batchsize x action + quantile_values_mid = tf.reshape(quantile_values_mid, [-1, num_actions]) #32 x batchsize x action + + quantiles_mid = tf.reshape(quantiles_mid, [-1, 1]) #32 x batchsize x action + #quantile_values = quantile_values_mid + #quantile = quantile_mid + return network_type(quantile_values=quantile_values_mid, quantiles=quantiles_mid, quantile_values_origin=quantile_values_origin, quantiles_origin=quantiles_origin, Fv_diff=Fv_diff, v_diff=v_diff, quantile_values_mid=quantile_values_mid, quantiles_mid=quantiles_mid, L_tau=L_tau, gradient_tau=gradient_tau, quantile_tau=quantile_tau) + + +@gin.configurable +class AtariPreprocessing(object): + """A class implementing image preprocessing for Atari 2600 agents. + + Specifically, this provides the following subset from the JAIR paper + (Bellemare et al., 2013) and Nature DQN paper (Mnih et al., 2015): + + * Frame skipping (defaults to 4). + * Terminal signal when a life is lost (off by default). + * Grayscale and max-pooling of the last two frames. + * Downsample the screen to a square image (defaults to 84x84). + + More generally, this class follows the preprocessing guidelines set down in + Machado et al. (2018), "Revisiting the Arcade Learning Environment: + Evaluation Protocols and Open Problems for General Agents". + """ + + def __init__(self, environment, frame_skip=4, terminal_on_life_loss=False, + screen_size=84): + """Constructor for an Atari 2600 preprocessor. + + Args: + environment: Gym environment whose observations are preprocessed. + frame_skip: int, the frequency at which the agent experiences the game. + terminal_on_life_loss: bool, If True, the step() method returns + is_terminal=True whenever a life is lost. See Mnih et al. 2015. + screen_size: int, size of a resized Atari 2600 frame. + + Raises: + ValueError: if frame_skip or screen_size are not strictly positive. + """ + if frame_skip <= 0: + raise ValueError('Frame skip should be strictly positive, got {}'. + format(frame_skip)) + if screen_size <= 0: + raise ValueError('Target screen size should be strictly positive, got {}'. + format(screen_size)) + + self.environment = environment + self.terminal_on_life_loss = terminal_on_life_loss + self.frame_skip = frame_skip + self.screen_size = screen_size + + obs_dims = self.environment.observation_space + # Stores temporary observations used for pooling over two successive + # frames. + self.screen_buffer = [ + np.empty((obs_dims.shape[0], obs_dims.shape[1]), dtype=np.uint8), + np.empty((obs_dims.shape[0], obs_dims.shape[1]), dtype=np.uint8) + ] + + self.game_over = False + self.lives = 0 # Will need to be set by reset(). + + @property + def observation_space(self): + # Return the observation space adjusted to match the shape of the processed + # observations. + return Box(low=0, high=255, shape=(self.screen_size, self.screen_size, 1), + dtype=np.uint8) + + @property + def action_space(self): + return self.environment.action_space + + @property + def reward_range(self): + return self.environment.reward_range + + @property + def metadata(self): + return self.environment.metadata + + def reset(self): + """Resets the environment. + + Returns: + observation: numpy array, the initial observation emitted by the + environment. + """ + self.environment.reset() + self.lives = self.environment.ale.lives() + self._fetch_grayscale_observation(self.screen_buffer[0]) + self.screen_buffer[1].fill(0) + return self._pool_and_resize() + + def render(self, mode): + """Renders the current screen, before preprocessing. + + This calls the Gym API's render() method. + + Args: + mode: Mode argument for the environment's render() method. + Valid values (str) are: + 'rgb_array': returns the raw ALE image. + 'human': renders to display via the Gym renderer. + + Returns: + if mode='rgb_array': numpy array, the most recent screen. + if mode='human': bool, whether the rendering was successful. + """ + return self.environment.render(mode) + + def step(self, action): + """Applies the given action in the environment. + + Remarks: + + * If a terminal state (from life loss or episode end) is reached, this may + execute fewer than self.frame_skip steps in the environment. + * Furthermore, in this case the returned observation may not contain valid + image data and should be ignored. + + Args: + action: The action to be executed. + + Returns: + observation: numpy array, the observation following the action. + reward: float, the reward following the action. + is_terminal: bool, whether the environment has reached a terminal state. + This is true when a life is lost and terminal_on_life_loss, or when the + episode is over. + info: Gym API's info data structure. + """ + accumulated_reward = 0. + + for time_step in range(self.frame_skip): + # We bypass the Gym observation altogether and directly fetch the + # grayscale image from the ALE. This is a little faster. + _, reward, game_over, info = self.environment.step(action) + accumulated_reward += reward + + if self.terminal_on_life_loss: + new_lives = self.environment.ale.lives() + is_terminal = game_over or new_lives < self.lives + self.lives = new_lives + else: + is_terminal = game_over + + if is_terminal: + break + # We max-pool over the last two frames, in grayscale. + elif time_step >= self.frame_skip - 2: + t = time_step - (self.frame_skip - 2) + self._fetch_grayscale_observation(self.screen_buffer[t]) + + # Pool the last two observations. + observation = self._pool_and_resize() + + self.game_over = game_over + return observation, accumulated_reward, is_terminal, info + + def _fetch_grayscale_observation(self, output): + """Returns the current observation in grayscale. + + The returned observation is stored in 'output'. + + Args: + output: numpy array, screen buffer to hold the returned observation. + + Returns: + observation: numpy array, the current observation in grayscale. + """ + self.environment.ale.getScreenGrayscale(output) + return output + + def _pool_and_resize(self): + """Transforms two frames into a Nature DQN observation. + + For efficiency, the transformation is done in-place in self.screen_buffer. + + Returns: + transformed_screen: numpy array, pooled, resized screen. + """ + # Pool if there are enough screens to do so. + if self.frame_skip > 1: + np.maximum(self.screen_buffer[0], self.screen_buffer[1], + out=self.screen_buffer[0]) + + transformed_image = cv2.resize(self.screen_buffer[0], + (self.screen_size, self.screen_size), + interpolation=cv2.INTER_AREA) + int_image = np.asarray(transformed_image, dtype=np.uint8) + return np.expand_dims(int_image, axis=2) diff --git a/dopamine/discrete_domains/checkpointer.py b/dopamine/discrete_domains/checkpointer.py index 08a478a..e64ab7e 100644 --- a/dopamine/discrete_domains/checkpointer.py +++ b/dopamine/discrete_domains/checkpointer.py @@ -1,177 +1,177 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""A checkpointing mechanism for Dopamine agents. - -This Checkpointer expects a base directory where checkpoints for different -iterations are stored. Specifically, Checkpointer.save_checkpoint() takes in -as input a dictionary 'data' to be pickled to disk. At each iteration, we -write a file called 'cpkt.#', where # is the iteration number. The -Checkpointer also cleans up old files, maintaining up to the CHECKPOINT_DURATION -most recent iterations. - -The Checkpointer writes a sentinel file to indicate that checkpointing was -globally successful. This means that all other checkpointing activities -(saving the Tensorflow graph, the replay buffer) should be performed *prior* -to calling Checkpointer.save_checkpoint(). This allows the Checkpointer to -detect incomplete checkpoints. - -#### Example - -After running 10 iterations (numbered 0...9) with base_directory='/checkpoint', -the following files will exist: -``` - /checkpoint/cpkt.6 - /checkpoint/cpkt.7 - /checkpoint/cpkt.8 - /checkpoint/cpkt.9 - /checkpoint/sentinel_checkpoint_complete.6 - /checkpoint/sentinel_checkpoint_complete.7 - /checkpoint/sentinel_checkpoint_complete.8 - /checkpoint/sentinel_checkpoint_complete.9 -``` -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import pickle -import tensorflow as tf - -CHECKPOINT_DURATION = 4 - - -def get_latest_checkpoint_number(base_directory): - """Returns the version number of the latest completed checkpoint. - - Args: - base_directory: str, directory in which to look for checkpoint files. - - Returns: - int, the iteration number of the latest checkpoint, or -1 if none was found. - """ - glob = os.path.join(base_directory, 'sentinel_checkpoint_complete.*') - def extract_iteration(x): - return int(x[x.rfind('.') + 1:]) - try: - checkpoint_files = tf.gfile.Glob(glob) - except tf.errors.NotFoundError: - return -1 - try: - latest_iteration = max(extract_iteration(x) for x in checkpoint_files) - return latest_iteration - except ValueError: - return -1 - - -class Checkpointer(object): - """Class for managing checkpoints for Dopamine agents. - """ - - def __init__(self, base_directory, checkpoint_file_prefix='ckpt', - checkpoint_frequency=1): - """Initializes Checkpointer. - - Args: - base_directory: str, directory where all checkpoints are saved/loaded. - checkpoint_file_prefix: str, prefix to use for naming checkpoint files. - checkpoint_frequency: int, the frequency at which to checkpoint. - - Raises: - ValueError: if base_directory is empty, or not creatable. - """ - if not base_directory: - raise ValueError('No path provided to Checkpointer.') - self._checkpoint_file_prefix = checkpoint_file_prefix - self._checkpoint_frequency = checkpoint_frequency - self._base_directory = base_directory - try: - tf.gfile.MakeDirs(base_directory) - except tf.errors.PermissionDeniedError: - # We catch the PermissionDeniedError and issue a more useful exception. - raise ValueError('Unable to create checkpoint path: {}.'.format( - base_directory)) - - def _generate_filename(self, file_prefix, iteration_number): - """Returns a checkpoint filename from prefix and iteration number.""" - filename = '{}.{}'.format(file_prefix, iteration_number) - return os.path.join(self._base_directory, filename) - - def _save_data_to_file(self, data, filename): - """Saves the given 'data' object to a file.""" - with tf.gfile.GFile(filename, 'w') as fout: - pickle.dump(data, fout) - - def save_checkpoint(self, iteration_number, data): - """Saves a new checkpoint at the current iteration_number. - - Args: - iteration_number: int, the current iteration number for this checkpoint. - data: Any (picklable) python object containing the data to store in the - checkpoint. - """ - if iteration_number % self._checkpoint_frequency != 0: - return - - filename = self._generate_filename(self._checkpoint_file_prefix, - iteration_number) - self._save_data_to_file(data, filename) - filename = self._generate_filename('sentinel_checkpoint_complete', - iteration_number) - with tf.gfile.GFile(filename, 'wb') as fout: - fout.write('done') - - self._clean_up_old_checkpoints(iteration_number) - - def _clean_up_old_checkpoints(self, iteration_number): - """Removes sufficiently old checkpoints.""" - # After writing a the checkpoint and sentinel file, we garbage collect files - # that are CHECKPOINT_DURATION * self._checkpoint_frequency versions old. - stale_iteration_number = iteration_number - (self._checkpoint_frequency * - CHECKPOINT_DURATION) - - if stale_iteration_number >= 0: - stale_file = self._generate_filename(self._checkpoint_file_prefix, - stale_iteration_number) - stale_sentinel = self._generate_filename('sentinel_checkpoint_complete', - stale_iteration_number) - try: - tf.gfile.Remove(stale_file) - tf.gfile.Remove(stale_sentinel) - except tf.errors.NotFoundError: - # Ignore if file not found. - tf.logging.info('Unable to remove {} or {}.'.format(stale_file, - stale_sentinel)) - - def _load_data_from_file(self, filename): - if not tf.gfile.Exists(filename): - return None - with tf.gfile.GFile(filename, 'rb') as fin: - return pickle.load(fin) - - def load_checkpoint(self, iteration_number): - """Tries to reload a checkpoint at the selected iteration number. - - Args: - iteration_number: The checkpoint iteration number to try to load. - - Returns: - If the checkpoint files exist, two unpickled objects that were passed in - as data to save_checkpoint; returns None if the files do not exist. - """ - checkpoint_file = self._generate_filename(self._checkpoint_file_prefix, - iteration_number) - return self._load_data_from_file(checkpoint_file) +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""A checkpointing mechanism for Dopamine agents. + +This Checkpointer expects a base directory where checkpoints for different +iterations are stored. Specifically, Checkpointer.save_checkpoint() takes in +as input a dictionary 'data' to be pickled to disk. At each iteration, we +write a file called 'cpkt.#', where # is the iteration number. The +Checkpointer also cleans up old files, maintaining up to the CHECKPOINT_DURATION +most recent iterations. + +The Checkpointer writes a sentinel file to indicate that checkpointing was +globally successful. This means that all other checkpointing activities +(saving the Tensorflow graph, the replay buffer) should be performed *prior* +to calling Checkpointer.save_checkpoint(). This allows the Checkpointer to +detect incomplete checkpoints. + +#### Example + +After running 10 iterations (numbered 0...9) with base_directory='/checkpoint', +the following files will exist: +``` + /checkpoint/cpkt.6 + /checkpoint/cpkt.7 + /checkpoint/cpkt.8 + /checkpoint/cpkt.9 + /checkpoint/sentinel_checkpoint_complete.6 + /checkpoint/sentinel_checkpoint_complete.7 + /checkpoint/sentinel_checkpoint_complete.8 + /checkpoint/sentinel_checkpoint_complete.9 +``` +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import pickle +import tensorflow as tf + +CHECKPOINT_DURATION = 4 + + +def get_latest_checkpoint_number(base_directory): + """Returns the version number of the latest completed checkpoint. + + Args: + base_directory: str, directory in which to look for checkpoint files. + + Returns: + int, the iteration number of the latest checkpoint, or -1 if none was found. + """ + glob = os.path.join(base_directory, 'sentinel_checkpoint_complete.*') + def extract_iteration(x): + return int(x[x.rfind('.') + 1:]) + try: + checkpoint_files = tf.gfile.Glob(glob) + except tf.errors.NotFoundError: + return -1 + try: + latest_iteration = max(extract_iteration(x) for x in checkpoint_files) + return latest_iteration + except ValueError: + return -1 + + +class Checkpointer(object): + """Class for managing checkpoints for Dopamine agents. + """ + + def __init__(self, base_directory, checkpoint_file_prefix='ckpt', + checkpoint_frequency=1): + """Initializes Checkpointer. + + Args: + base_directory: str, directory where all checkpoints are saved/loaded. + checkpoint_file_prefix: str, prefix to use for naming checkpoint files. + checkpoint_frequency: int, the frequency at which to checkpoint. + + Raises: + ValueError: if base_directory is empty, or not creatable. + """ + if not base_directory: + raise ValueError('No path provided to Checkpointer.') + self._checkpoint_file_prefix = checkpoint_file_prefix + self._checkpoint_frequency = checkpoint_frequency + self._base_directory = base_directory + try: + tf.gfile.MakeDirs(base_directory) + except tf.errors.PermissionDeniedError: + # We catch the PermissionDeniedError and issue a more useful exception. + raise ValueError('Unable to create checkpoint path: {}.'.format( + base_directory)) + + def _generate_filename(self, file_prefix, iteration_number): + """Returns a checkpoint filename from prefix and iteration number.""" + filename = '{}.{}'.format(file_prefix, iteration_number) + return os.path.join(self._base_directory, filename) + + def _save_data_to_file(self, data, filename): + """Saves the given 'data' object to a file.""" + with tf.gfile.GFile(filename, 'w') as fout: + pickle.dump(data, fout) + + def save_checkpoint(self, iteration_number, data): + """Saves a new checkpoint at the current iteration_number. + + Args: + iteration_number: int, the current iteration number for this checkpoint. + data: Any (picklable) python object containing the data to store in the + checkpoint. + """ + if iteration_number % self._checkpoint_frequency != 0: + return + + filename = self._generate_filename(self._checkpoint_file_prefix, + iteration_number) + self._save_data_to_file(data, filename) + filename = self._generate_filename('sentinel_checkpoint_complete', + iteration_number) + with tf.gfile.GFile(filename, 'wb') as fout: + fout.write('done') + + self._clean_up_old_checkpoints(iteration_number) + + def _clean_up_old_checkpoints(self, iteration_number): + """Removes sufficiently old checkpoints.""" + # After writing a the checkpoint and sentinel file, we garbage collect files + # that are CHECKPOINT_DURATION * self._checkpoint_frequency versions old. + stale_iteration_number = iteration_number - (self._checkpoint_frequency * + CHECKPOINT_DURATION) + + if stale_iteration_number >= 0: + stale_file = self._generate_filename(self._checkpoint_file_prefix, + stale_iteration_number) + stale_sentinel = self._generate_filename('sentinel_checkpoint_complete', + stale_iteration_number) + try: + tf.gfile.Remove(stale_file) + tf.gfile.Remove(stale_sentinel) + except tf.errors.NotFoundError: + # Ignore if file not found. + tf.logging.info('Unable to remove {} or {}.'.format(stale_file, + stale_sentinel)) + + def _load_data_from_file(self, filename): + if not tf.gfile.Exists(filename): + return None + with tf.gfile.GFile(filename, 'rb') as fin: + return pickle.load(fin) + + def load_checkpoint(self, iteration_number): + """Tries to reload a checkpoint at the selected iteration number. + + Args: + iteration_number: The checkpoint iteration number to try to load. + + Returns: + If the checkpoint files exist, two unpickled objects that were passed in + as data to save_checkpoint; returns None if the files do not exist. + """ + checkpoint_file = self._generate_filename(self._checkpoint_file_prefix, + iteration_number) + return self._load_data_from_file(checkpoint_file) diff --git a/dopamine/discrete_domains/gym_lib.py b/dopamine/discrete_domains/gym_lib.py index 01e3dff..72c6ad2 100644 --- a/dopamine/discrete_domains/gym_lib.py +++ b/dopamine/discrete_domains/gym_lib.py @@ -1,335 +1,335 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Gym-specific (non-Atari) utilities. - -Some network specifications specific to certain Gym environments are provided -here. - -Includes a wrapper class around Gym environments. This class makes general Gym -environments conformant with the API Dopamine is expecting. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import itertools -import math - - - -import gym -import numpy as np -import tensorflow as tf - -import gin.tf - - -CARTPOLE_MIN_VALS = np.array([-2.4, -5., -math.pi/12., -math.pi*2.]) -CARTPOLE_MAX_VALS = np.array([2.4, 5., math.pi/12., math.pi*2.]) -ACROBOT_MIN_VALS = np.array([-1., -1., -1., -1., -5., -5.]) -ACROBOT_MAX_VALS = np.array([1., 1., 1., 1., 5., 5.]) -gin.constant('gym_lib.CARTPOLE_OBSERVATION_SHAPE', (4, 1)) -gin.constant('gym_lib.CARTPOLE_OBSERVATION_DTYPE', tf.float32) -gin.constant('gym_lib.CARTPOLE_STACK_SIZE', 1) -gin.constant('gym_lib.ACROBOT_OBSERVATION_SHAPE', (6, 1)) -gin.constant('gym_lib.ACROBOT_OBSERVATION_DTYPE', tf.float32) -gin.constant('gym_lib.ACROBOT_STACK_SIZE', 1) - -slim = tf.contrib.slim - - -@gin.configurable -def create_gym_environment(environment_name=None, version='v0'): - """Wraps a Gym environment with some basic preprocessing. - - Args: - environment_name: str, the name of the environment to run. - version: str, version of the environment to run. - - Returns: - A Gym environment with some standard preprocessing. - """ - assert environment_name is not None - full_game_name = '{}-{}'.format(environment_name, version) - env = gym.make(full_game_name) - # Strip out the TimeLimit wrapper from Gym, which caps us at 200 steps. - env = env.env - # Wrap the returned environment in a class which conforms to the API expected - # by Dopamine. - env = GymPreprocessing(env) - return env - - -@gin.configurable -def _basic_discrete_domain_network(min_vals, max_vals, num_actions, state, - num_atoms=None): - """Builds a basic network for discrete domains, rescaling inputs to [-1, 1]. - - Args: - min_vals: float, minimum attainable values (must be same shape as `state`). - max_vals: float, maximum attainable values (must be same shape as `state`). - num_actions: int, number of actions. - state: `tf.Tensor`, the state input. - num_atoms: int or None, if None will construct a DQN-style network, - otherwise will construct a Rainbow-style network. - - Returns: - The Q-values for DQN-style agents or logits for Rainbow-style agents. - """ - net = tf.cast(state, tf.float32) - net = slim.flatten(net) - net -= min_vals - net /= max_vals - min_vals - net = 2.0 * net - 1.0 # Rescale in range [-1, 1]. - net = slim.fully_connected(net, 512) - net = slim.fully_connected(net, 512) - if num_atoms is None: - # We are constructing a DQN-style network. - return slim.fully_connected(net, num_actions, activation_fn=None) - else: - # We are constructing a rainbow-style network. - return slim.fully_connected(net, num_actions * num_atoms, - activation_fn=None) - - -@gin.configurable -def cartpole_dqn_network(num_actions, network_type, state): - """Builds the deep network used to compute the agent's Q-values. - - It rescales the input features to a range that yields improved performance. - - Args: - num_actions: int, number of actions. - network_type: namedtuple, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - q_values = _basic_discrete_domain_network( - CARTPOLE_MIN_VALS, CARTPOLE_MAX_VALS, num_actions, state) - return network_type(q_values) - - -class FourierBasis(object): - """Fourier Basis linear function approximation. - - Requires the ranges for each dimension, and is thus able to use only sine or - cosine (and uses cosine). So, this has half the coefficients that a full - Fourier approximation would use. - - Many thanks to Will Dabney (wdabney@) for this implementation. - - From the paper: - G.D. Konidaris, S. Osentoski and P.S. Thomas. (2011) - Value Function Approximation in Reinforcement Learning using the Fourier Basis - """ - - def __init__(self, nvars, min_vals=0, max_vals=None, order=3): - self.order = order - self.min_vals = min_vals - self.max_vals = max_vals - terms = itertools.product(range(order + 1), repeat=nvars) - - # Removing first iterate because it corresponds to the constant bias - self.multipliers = tf.constant( - [list(map(int, x)) for x in terms][1:], dtype=tf.float32) - - def scale(self, values): - shifted = values - self.min_vals - if self.max_vals is None: - return shifted - - return shifted / (self.max_vals - self.min_vals) - - def compute_features(self, features): - # Important to rescale features to be between [0,1] - scaled = self.scale(features) - return tf.cos(np.pi * tf.matmul(scaled, self.multipliers, transpose_b=True)) - - -@gin.configurable -def fourier_dqn_network(min_vals, - max_vals, - num_actions, - state, - fourier_basis_order=3): - """Builds the function approximator used to compute the agent's Q-values. - - It uses FourierBasis features and a linear layer. - - Args: - min_vals: float, minimum attainable values (must be same shape as `state`). - max_vals: float, maximum attainable values (must be same shape as `state`). - num_actions: int, number of actions. - state: `tf.Tensor`, contains the agent's current state. - fourier_basis_order: int, order of the Fourier basis functions. - - Returns: - The Q-values for DQN-style agents or logits for Rainbow-style agents. - """ - net = tf.cast(state, tf.float32) - net = slim.flatten(net) - - # Feed state through Fourier basis. - feature_generator = FourierBasis( - net.get_shape().as_list()[-1], - min_vals, - max_vals, - order=fourier_basis_order) - net = feature_generator.compute_features(net) - - # Q-values are always linear w.r.t. last layer. - q_values = slim.fully_connected( - net, num_actions, activation_fn=None, biases_initializer=None) - return q_values - - -def cartpole_fourier_dqn_network(num_actions, network_type, state): - """Builds the function approximator used to compute the agent's Q-values. - - It uses the Fourier basis features and a linear function approximator. - - Args: - num_actions: int, number of actions. - network_type: namedtuple, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - q_values = fourier_dqn_network(CARTPOLE_MIN_VALS, CARTPOLE_MAX_VALS, - num_actions, state) - return network_type(q_values) - - -@gin.configurable -def cartpole_rainbow_network(num_actions, num_atoms, support, network_type, - state): - """Build the deep network used to compute the agent's Q-value distributions. - - Args: - num_actions: int, number of actions. - num_atoms: int, the number of buckets of the value function distribution. - support: tf.linspace, the support of the Q-value distribution. - network_type: `namedtuple`, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - net = _basic_discrete_domain_network( - CARTPOLE_MIN_VALS, CARTPOLE_MAX_VALS, num_actions, state, - num_atoms=num_atoms) - logits = tf.reshape(net, [-1, num_actions, num_atoms]) - probabilities = tf.contrib.layers.softmax(logits) - q_values = tf.reduce_sum(support * probabilities, axis=2) - return network_type(q_values, logits, probabilities) - - -@gin.configurable -def acrobot_dqn_network(num_actions, network_type, state): - """Builds the deep network used to compute the agent's Q-values. - - It rescales the input features to a range that yields improved performance. - - Args: - num_actions: int, number of actions. - network_type: namedtuple, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - q_values = _basic_discrete_domain_network( - ACROBOT_MIN_VALS, ACROBOT_MAX_VALS, num_actions, state) - return network_type(q_values) - - -@gin.configurable -def acrobot_fourier_dqn_network(num_actions, network_type, state): - """Builds the function approximator used to compute the agent's Q-values. - - It uses the Fourier basis features and a linear function approximator. - - Args: - num_actions: int, number of actions. - network_type: namedtuple, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - q_values = fourier_dqn_network(ACROBOT_MIN_VALS, ACROBOT_MAX_VALS, - num_actions, state) - return network_type(q_values) - - -@gin.configurable -def acrobot_rainbow_network(num_actions, num_atoms, support, network_type, - state): - """Build the deep network used to compute the agent's Q-value distributions. - - Args: - num_actions: int, number of actions. - num_atoms: int, the number of buckets of the value function distribution. - support: tf.linspace, the support of the Q-value distribution. - network_type: `namedtuple`, collection of expected values to return. - state: `tf.Tensor`, contains the agent's current state. - - Returns: - net: _network_type object containing the tensors output by the network. - """ - net = _basic_discrete_domain_network( - ACROBOT_MIN_VALS, ACROBOT_MAX_VALS, num_actions, state, - num_atoms=num_atoms) - logits = tf.reshape(net, [-1, num_actions, num_atoms]) - probabilities = tf.contrib.layers.softmax(logits) - q_values = tf.reduce_sum(support * probabilities, axis=2) - return network_type(q_values, logits, probabilities) - - -@gin.configurable -class GymPreprocessing(object): - """A Wrapper class around Gym environments.""" - - def __init__(self, environment): - self.environment = environment - self.game_over = False - - @property - def observation_space(self): - return self.environment.observation_space - - @property - def action_space(self): - return self.environment.action_space - - @property - def reward_range(self): - return self.environment.reward_range - - @property - def metadata(self): - return self.environment.metadata - - def reset(self): - return self.environment.reset() - - def step(self, action): - observation, reward, game_over, info = self.environment.step(action) - self.game_over = game_over - return observation, reward, game_over, info +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Gym-specific (non-Atari) utilities. + +Some network specifications specific to certain Gym environments are provided +here. + +Includes a wrapper class around Gym environments. This class makes general Gym +environments conformant with the API Dopamine is expecting. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import itertools +import math + + + +import gym +import numpy as np +import tensorflow as tf + +import gin.tf + + +CARTPOLE_MIN_VALS = np.array([-2.4, -5., -math.pi/12., -math.pi*2.]) +CARTPOLE_MAX_VALS = np.array([2.4, 5., math.pi/12., math.pi*2.]) +ACROBOT_MIN_VALS = np.array([-1., -1., -1., -1., -5., -5.]) +ACROBOT_MAX_VALS = np.array([1., 1., 1., 1., 5., 5.]) +gin.constant('gym_lib.CARTPOLE_OBSERVATION_SHAPE', (4, 1)) +gin.constant('gym_lib.CARTPOLE_OBSERVATION_DTYPE', tf.float32) +gin.constant('gym_lib.CARTPOLE_STACK_SIZE', 1) +gin.constant('gym_lib.ACROBOT_OBSERVATION_SHAPE', (6, 1)) +gin.constant('gym_lib.ACROBOT_OBSERVATION_DTYPE', tf.float32) +gin.constant('gym_lib.ACROBOT_STACK_SIZE', 1) + +slim = tf.contrib.slim + + +@gin.configurable +def create_gym_environment(environment_name=None, version='v0'): + """Wraps a Gym environment with some basic preprocessing. + + Args: + environment_name: str, the name of the environment to run. + version: str, version of the environment to run. + + Returns: + A Gym environment with some standard preprocessing. + """ + assert environment_name is not None + full_game_name = '{}-{}'.format(environment_name, version) + env = gym.make(full_game_name) + # Strip out the TimeLimit wrapper from Gym, which caps us at 200 steps. + env = env.env + # Wrap the returned environment in a class which conforms to the API expected + # by Dopamine. + env = GymPreprocessing(env) + return env + + +@gin.configurable +def _basic_discrete_domain_network(min_vals, max_vals, num_actions, state, + num_atoms=None): + """Builds a basic network for discrete domains, rescaling inputs to [-1, 1]. + + Args: + min_vals: float, minimum attainable values (must be same shape as `state`). + max_vals: float, maximum attainable values (must be same shape as `state`). + num_actions: int, number of actions. + state: `tf.Tensor`, the state input. + num_atoms: int or None, if None will construct a DQN-style network, + otherwise will construct a Rainbow-style network. + + Returns: + The Q-values for DQN-style agents or logits for Rainbow-style agents. + """ + net = tf.cast(state, tf.float32) + net = slim.flatten(net) + net -= min_vals + net /= max_vals - min_vals + net = 2.0 * net - 1.0 # Rescale in range [-1, 1]. + net = slim.fully_connected(net, 512) + net = slim.fully_connected(net, 512) + if num_atoms is None: + # We are constructing a DQN-style network. + return slim.fully_connected(net, num_actions, activation_fn=None) + else: + # We are constructing a rainbow-style network. + return slim.fully_connected(net, num_actions * num_atoms, + activation_fn=None) + + +@gin.configurable +def cartpole_dqn_network(num_actions, network_type, state): + """Builds the deep network used to compute the agent's Q-values. + + It rescales the input features to a range that yields improved performance. + + Args: + num_actions: int, number of actions. + network_type: namedtuple, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + q_values = _basic_discrete_domain_network( + CARTPOLE_MIN_VALS, CARTPOLE_MAX_VALS, num_actions, state) + return network_type(q_values) + + +class FourierBasis(object): + """Fourier Basis linear function approximation. + + Requires the ranges for each dimension, and is thus able to use only sine or + cosine (and uses cosine). So, this has half the coefficients that a full + Fourier approximation would use. + + Many thanks to Will Dabney (wdabney@) for this implementation. + + From the paper: + G.D. Konidaris, S. Osentoski and P.S. Thomas. (2011) + Value Function Approximation in Reinforcement Learning using the Fourier Basis + """ + + def __init__(self, nvars, min_vals=0, max_vals=None, order=3): + self.order = order + self.min_vals = min_vals + self.max_vals = max_vals + terms = itertools.product(range(order + 1), repeat=nvars) + + # Removing first iterate because it corresponds to the constant bias + self.multipliers = tf.constant( + [list(map(int, x)) for x in terms][1:], dtype=tf.float32) + + def scale(self, values): + shifted = values - self.min_vals + if self.max_vals is None: + return shifted + + return shifted / (self.max_vals - self.min_vals) + + def compute_features(self, features): + # Important to rescale features to be between [0,1] + scaled = self.scale(features) + return tf.cos(np.pi * tf.matmul(scaled, self.multipliers, transpose_b=True)) + + +@gin.configurable +def fourier_dqn_network(min_vals, + max_vals, + num_actions, + state, + fourier_basis_order=3): + """Builds the function approximator used to compute the agent's Q-values. + + It uses FourierBasis features and a linear layer. + + Args: + min_vals: float, minimum attainable values (must be same shape as `state`). + max_vals: float, maximum attainable values (must be same shape as `state`). + num_actions: int, number of actions. + state: `tf.Tensor`, contains the agent's current state. + fourier_basis_order: int, order of the Fourier basis functions. + + Returns: + The Q-values for DQN-style agents or logits for Rainbow-style agents. + """ + net = tf.cast(state, tf.float32) + net = slim.flatten(net) + + # Feed state through Fourier basis. + feature_generator = FourierBasis( + net.get_shape().as_list()[-1], + min_vals, + max_vals, + order=fourier_basis_order) + net = feature_generator.compute_features(net) + + # Q-values are always linear w.r.t. last layer. + q_values = slim.fully_connected( + net, num_actions, activation_fn=None, biases_initializer=None) + return q_values + + +def cartpole_fourier_dqn_network(num_actions, network_type, state): + """Builds the function approximator used to compute the agent's Q-values. + + It uses the Fourier basis features and a linear function approximator. + + Args: + num_actions: int, number of actions. + network_type: namedtuple, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + q_values = fourier_dqn_network(CARTPOLE_MIN_VALS, CARTPOLE_MAX_VALS, + num_actions, state) + return network_type(q_values) + + +@gin.configurable +def cartpole_rainbow_network(num_actions, num_atoms, support, network_type, + state): + """Build the deep network used to compute the agent's Q-value distributions. + + Args: + num_actions: int, number of actions. + num_atoms: int, the number of buckets of the value function distribution. + support: tf.linspace, the support of the Q-value distribution. + network_type: `namedtuple`, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + net = _basic_discrete_domain_network( + CARTPOLE_MIN_VALS, CARTPOLE_MAX_VALS, num_actions, state, + num_atoms=num_atoms) + logits = tf.reshape(net, [-1, num_actions, num_atoms]) + probabilities = tf.contrib.layers.softmax(logits) + q_values = tf.reduce_sum(support * probabilities, axis=2) + return network_type(q_values, logits, probabilities) + + +@gin.configurable +def acrobot_dqn_network(num_actions, network_type, state): + """Builds the deep network used to compute the agent's Q-values. + + It rescales the input features to a range that yields improved performance. + + Args: + num_actions: int, number of actions. + network_type: namedtuple, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + q_values = _basic_discrete_domain_network( + ACROBOT_MIN_VALS, ACROBOT_MAX_VALS, num_actions, state) + return network_type(q_values) + + +@gin.configurable +def acrobot_fourier_dqn_network(num_actions, network_type, state): + """Builds the function approximator used to compute the agent's Q-values. + + It uses the Fourier basis features and a linear function approximator. + + Args: + num_actions: int, number of actions. + network_type: namedtuple, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + q_values = fourier_dqn_network(ACROBOT_MIN_VALS, ACROBOT_MAX_VALS, + num_actions, state) + return network_type(q_values) + + +@gin.configurable +def acrobot_rainbow_network(num_actions, num_atoms, support, network_type, + state): + """Build the deep network used to compute the agent's Q-value distributions. + + Args: + num_actions: int, number of actions. + num_atoms: int, the number of buckets of the value function distribution. + support: tf.linspace, the support of the Q-value distribution. + network_type: `namedtuple`, collection of expected values to return. + state: `tf.Tensor`, contains the agent's current state. + + Returns: + net: _network_type object containing the tensors output by the network. + """ + net = _basic_discrete_domain_network( + ACROBOT_MIN_VALS, ACROBOT_MAX_VALS, num_actions, state, + num_atoms=num_atoms) + logits = tf.reshape(net, [-1, num_actions, num_atoms]) + probabilities = tf.contrib.layers.softmax(logits) + q_values = tf.reduce_sum(support * probabilities, axis=2) + return network_type(q_values, logits, probabilities) + + +@gin.configurable +class GymPreprocessing(object): + """A Wrapper class around Gym environments.""" + + def __init__(self, environment): + self.environment = environment + self.game_over = False + + @property + def observation_space(self): + return self.environment.observation_space + + @property + def action_space(self): + return self.environment.action_space + + @property + def reward_range(self): + return self.environment.reward_range + + @property + def metadata(self): + return self.environment.metadata + + def reset(self): + return self.environment.reset() + + def step(self, action): + observation, reward, game_over, info = self.environment.step(action) + self.game_over = game_over + return observation, reward, game_over, info diff --git a/dopamine/discrete_domains/iteration_statistics.py b/dopamine/discrete_domains/iteration_statistics.py index f47c575..a7526cc 100644 --- a/dopamine/discrete_domains/iteration_statistics.py +++ b/dopamine/discrete_domains/iteration_statistics.py @@ -1,49 +1,49 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""A class for storing iteration-specific metrics. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - -class IterationStatistics(object): - """A class for storing iteration-specific metrics. - - The internal format is as follows: we maintain a mapping from keys to lists. - Each list contains all the values corresponding to the given key. - - For example, self.data_lists['train_episode_returns'] might contain the - per-episode returns achieved during this iteration. - - Attributes: - data_lists: dict mapping each metric_name (str) to a list of said metric - across episodes. - """ - - def __init__(self): - self.data_lists = {} - - def append(self, data_pairs): - """Add the given values to their corresponding key-indexed lists. - - Args: - data_pairs: A dictionary of key-value pairs to be recorded. - """ - for key, value in data_pairs.items(): - if key not in self.data_lists: - self.data_lists[key] = [] - self.data_lists[key].append(value) +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""A class for storing iteration-specific metrics. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +class IterationStatistics(object): + """A class for storing iteration-specific metrics. + + The internal format is as follows: we maintain a mapping from keys to lists. + Each list contains all the values corresponding to the given key. + + For example, self.data_lists['train_episode_returns'] might contain the + per-episode returns achieved during this iteration. + + Attributes: + data_lists: dict mapping each metric_name (str) to a list of said metric + across episodes. + """ + + def __init__(self): + self.data_lists = {} + + def append(self, data_pairs): + """Add the given values to their corresponding key-indexed lists. + + Args: + data_pairs: A dictionary of key-value pairs to be recorded. + """ + for key, value in data_pairs.items(): + if key not in self.data_lists: + self.data_lists[key] = [] + self.data_lists[key].append(value) diff --git a/dopamine/discrete_domains/logger.py b/dopamine/discrete_domains/logger.py index 8e1b51b..bd86913 100644 --- a/dopamine/discrete_domains/logger.py +++ b/dopamine/discrete_domains/logger.py @@ -1,105 +1,105 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""A lightweight logging mechanism for dopamine agents.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import pickle -import tensorflow as tf - - -CHECKPOINT_DURATION = 4 - - -class Logger(object): - """Class for maintaining a dictionary of data to log.""" - - def __init__(self, logging_dir): - """Initializes Logger. - - Args: - logging_dir: str, Directory to which logs are written. - """ - # Dict used by logger to store data. - self.data = {} - self._logging_enabled = True - - if not logging_dir: - tf.logging.info('Logging directory not specified, will not log.') - self._logging_enabled = False - return - # Try to create logging directory. - try: - tf.gfile.MakeDirs(logging_dir) - except tf.errors.PermissionDeniedError: - # If it already exists, ignore exception. - pass - if not tf.gfile.Exists(logging_dir): - tf.logging.warning( - 'Could not create directory %s, logging will be disabled.', - logging_dir) - self._logging_enabled = False - return - self._logging_dir = logging_dir - - def __setitem__(self, key, value): - """This method will set an entry at key with value in the dictionary. - - It will effectively overwrite any previous data at the same key. - - Args: - key: str, indicating key where to write the entry. - value: A python object to store. - """ - if self._logging_enabled: - self.data[key] = value - - def _generate_filename(self, filename_prefix, iteration_number): - filename = '{}_{}'.format(filename_prefix, iteration_number) - return os.path.join(self._logging_dir, filename) - - def log_to_file(self, filename_prefix, iteration_number): - """Save the pickled dictionary to a file. - - Args: - filename_prefix: str, name of the file to use (without iteration - number). - iteration_number: int, the iteration number, appended to the end of - filename_prefix. - """ - if not self._logging_enabled: - tf.logging.warning('Logging is disabled.') - return - log_file = self._generate_filename(filename_prefix, iteration_number) - with tf.gfile.GFile(log_file, 'w') as fout: - pickle.dump(self.data, fout, protocol=pickle.HIGHEST_PROTOCOL) - # After writing a checkpoint file, we garbage collect the log file - # that is CHECKPOINT_DURATION versions old. - stale_iteration_number = iteration_number - CHECKPOINT_DURATION - if stale_iteration_number >= 0: - stale_file = self._generate_filename(filename_prefix, - stale_iteration_number) - try: - tf.gfile.Remove(stale_file) - except tf.errors.NotFoundError: - # Ignore if file not found. - pass - - def is_logging_enabled(self): - """Return if logging is enabled.""" - return self._logging_enabled +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""A lightweight logging mechanism for dopamine agents.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import pickle +import tensorflow as tf + + +CHECKPOINT_DURATION = 4 + + +class Logger(object): + """Class for maintaining a dictionary of data to log.""" + + def __init__(self, logging_dir): + """Initializes Logger. + + Args: + logging_dir: str, Directory to which logs are written. + """ + # Dict used by logger to store data. + self.data = {} + self._logging_enabled = True + + if not logging_dir: + tf.logging.info('Logging directory not specified, will not log.') + self._logging_enabled = False + return + # Try to create logging directory. + try: + tf.gfile.MakeDirs(logging_dir) + except tf.errors.PermissionDeniedError: + # If it already exists, ignore exception. + pass + if not tf.gfile.Exists(logging_dir): + tf.logging.warning( + 'Could not create directory %s, logging will be disabled.', + logging_dir) + self._logging_enabled = False + return + self._logging_dir = logging_dir + + def __setitem__(self, key, value): + """This method will set an entry at key with value in the dictionary. + + It will effectively overwrite any previous data at the same key. + + Args: + key: str, indicating key where to write the entry. + value: A python object to store. + """ + if self._logging_enabled: + self.data[key] = value + + def _generate_filename(self, filename_prefix, iteration_number): + filename = '{}_{}'.format(filename_prefix, iteration_number) + return os.path.join(self._logging_dir, filename) + + def log_to_file(self, filename_prefix, iteration_number): + """Save the pickled dictionary to a file. + + Args: + filename_prefix: str, name of the file to use (without iteration + number). + iteration_number: int, the iteration number, appended to the end of + filename_prefix. + """ + if not self._logging_enabled: + tf.logging.warning('Logging is disabled.') + return + log_file = self._generate_filename(filename_prefix, iteration_number) + with tf.gfile.GFile(log_file, 'w') as fout: + pickle.dump(self.data, fout, protocol=pickle.HIGHEST_PROTOCOL) + # After writing a checkpoint file, we garbage collect the log file + # that is CHECKPOINT_DURATION versions old. + stale_iteration_number = iteration_number - CHECKPOINT_DURATION + if stale_iteration_number >= 0: + stale_file = self._generate_filename(filename_prefix, + stale_iteration_number) + try: + tf.gfile.Remove(stale_file) + except tf.errors.NotFoundError: + # Ignore if file not found. + pass + + def is_logging_enabled(self): + """Return if logging is enabled.""" + return self._logging_enabled diff --git a/dopamine/discrete_domains/run-fqf.sh b/dopamine/discrete_domains/run-fqf.sh index 63ffad2..598b825 100644 --- a/dopamine/discrete_domains/run-fqf.sh +++ b/dopamine/discrete_domains/run-fqf.sh @@ -7,10 +7,13 @@ mkdir ../agents/fqf/configs/gins &> /dev/null n=0 #iqn_fqf-ws-sticky-0" "iqn_fqf-ws-sticky-0" declare -a games=("Centipede") -declare -a seeds=(0 1 2) -declare -a factors=(0.00001) -declare -a ents=(0.0001) +#Berzerk Gopher Kangaroo ChopperCommand Centipede Breakout Amidar KungFuMaster DoubleDunk +declare -a seeds=(0) +declare -a factors=(0.00001 0.000001) +declare -a ents=(0.0001 0.00001) declare -a optimizers=('rmsprop') +declare -a losses=('directbp' 'sqloss') + for game in "${games[@]}" do for opt in "${optimizers[@]}" @@ -21,12 +24,15 @@ do do for ent in "${ents[@]}" do - d="iqn_fqf-ws-${opt}-f${factor}-e${ent}-s${seed}" - sed -e "s!GAME!${game}!" -e "s!RUNTYPE!$d!" -e "s!FQFFACTOR!${factor}!" -e "s!FQFENT!${ent}!" ../agents/fqf/configs/fqf.gin > ../agents/fqf/configs/gins/${d}_${game}.gin - CUDA_VISIBLE_DEVICES=$n nohup python train.py --base_dir=/tmp/${d}-${game} --gin_files="../agents/fqf/configs/gins/${d}_${game}.gin" >& logs/output_${game}_${d} & - echo "$i, $n" - n=$((($n+1) % 4)) - sleep 2 + for loss in "${losses[@]}" + do + d="iqn_fqf-ws-${loss}-${opt}-f${factor}-e${ent}-s${seed}" + sed -e "s!GAME!${game}!" -e "s!RUNTYPE!$d!" -e "s!FQFFACTOR!${factor}!" -e "s!FQFENT!${ent}!" ../agents/fqf/configs/fqf.gin > ../agents/fqf/configs/gins/${d}_${game}.gin + CUDA_VISIBLE_DEVICES=$n nohup python train.py --base_dir=/tmp/${d}-${game} --gin_files="../agents/fqf/configs/gins/${d}_${game}.gin" >& logs/output_${game}_${d} & + echo "$d, $n" + n=$((($n+1) % 4)) + sleep 2 + done done done done diff --git a/dopamine/discrete_domains/run-iqn.sh b/dopamine/discrete_domains/run-iqn.sh index 3a80f60..76f997d 100644 --- a/dopamine/discrete_domains/run-iqn.sh +++ b/dopamine/discrete_domains/run-iqn.sh @@ -14,7 +14,7 @@ do d="iqn-s${seed}" sed -e "s!GAME!${game}!" -e "s!RUNTYPE!$d!" ../agents/implicit_quantile/configs/implicit_quantile_icml.gin > ../agents/implicit_quantile/configs/gins/${d}_icml_${game}.gin CUDA_VISIBLE_DEVICES=$n nohup python train.py --base_dir=/tmp/${d}-${game} --gin_files="../agents/implicit_quantile/configs/gins/${d}_icml_${game}.gin" >& logs/output_${game}_${d} & - echo "$i, $n" + echo "$d, $n" n=$(($n+1)) sleep 2 done diff --git a/dopamine/discrete_domains/run_experiment.py b/dopamine/discrete_domains/run_experiment.py index 0a9ac64..14d3f2d 100644 --- a/dopamine/discrete_domains/run_experiment.py +++ b/dopamine/discrete_domains/run_experiment.py @@ -1,594 +1,594 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Module defining classes and helper methods for general agents.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import sys -import time -import datetime -import csv, json, pickle - -from dopamine.agents.dqn import dqn_agent -from dopamine.agents.implicit_quantile import implicit_quantile_agent -from dopamine.agents.rainbow import rainbow_agent -from dopamine.discrete_domains import atari_lib -from dopamine.discrete_domains import checkpointer -from dopamine.discrete_domains import iteration_statistics -from dopamine.discrete_domains import logger - -import numpy as np -import tensorflow as tf - -import gin.tf - - -def load_gin_configs(gin_files, gin_bindings): - """Loads gin configuration files. - - Args: - gin_files: list, of paths to the gin configuration files for this - experiment. - gin_bindings: list, of gin parameter bindings to override the values in - the config files. - """ - gin.parse_config_files_and_bindings(gin_files, - bindings=gin_bindings, - skip_unknown=False) - - -@gin.configurable -def create_agent(sess, environment, agent_name=None, summary_writer=None, - debug_mode=False): - """Creates an agent. - - Args: - sess: A `tf.Session` object for running associated ops. - environment: A gym environment (e.g. Atari 2600). - agent_name: str, name of the agent to create. - summary_writer: A Tensorflow summary writer to pass to the agent - for in-agent training statistics in Tensorboard. - debug_mode: bool, whether to output Tensorboard summaries. If set to true, - the agent will output in-episode statistics to Tensorboard. Disabled by - default as this results in slower training. - - Returns: - agent: An RL agent. - - Raises: - ValueError: If `agent_name` is not in supported list. - """ - assert agent_name is not None - if not debug_mode: - summary_writer = None - if agent_name == 'dqn': - return dqn_agent.DQNAgent(sess, num_actions=environment.action_space.n, - summary_writer=summary_writer) - elif agent_name == 'rainbow': - return rainbow_agent.RainbowAgent( - sess, num_actions=environment.action_space.n, - summary_writer=summary_writer) - elif agent_name == 'implicit_quantile': - return implicit_quantile_agent.ImplicitQuantileAgent( - sess, num_actions=environment.action_space.n, - summary_writer=summary_writer) - else: - raise ValueError('Unknown agent: {}'.format(agent_name)) - - -@gin.configurable -def create_runner(base_dir, schedule='continuous_train_and_eval'): - """Creates an experiment Runner. - - Args: - base_dir: str, base directory for hosting all subdirectories. - schedule: string, which type of Runner to use. - - Returns: - runner: A `Runner` like object. - - Raises: - ValueError: When an unknown schedule is encountered. - """ - assert base_dir is not None - # Continuously runs training and evaluation until max num_iterations is hit. - if schedule == 'continuous_train_and_eval': - return Runner(base_dir, create_agent) - # Continuously runs training until max num_iterations is hit. - elif schedule == 'continuous_train': - return TrainRunner(base_dir, create_agent) - else: - raise ValueError('Unknown schedule: {}'.format(schedule)) - - -@gin.configurable -class Runner(object): - """Object that handles running Dopamine experiments. - - Here we use the term 'experiment' to mean simulating interactions between the - agent and the environment and reporting some statistics pertaining to these - interactions. - - A simple scenario to train a DQN agent is as follows: - - ```python - import dopamine.discrete_domains.atari_lib - base_dir = '/tmp/simple_example' - def create_agent(sess, environment): - return dqn_agent.DQNAgent(sess, num_actions=environment.action_space.n) - runner = Runner(base_dir, create_agent, atari_lib.create_atari_environment) - runner.run() - ``` - """ - - def __init__(self, - base_dir, - create_agent_fn, - create_environment_fn=atari_lib.create_atari_environment, - checkpoint_file_prefix='ckpt', - logging_file_prefix='log', - log_every_n=1, - num_iterations=200, - runtype='run', - game='Pong', - training_steps=250000, - evaluation_steps=125000, - max_steps_per_episode=27000): - """Initialize the Runner object in charge of running a full experiment. - - Args: - base_dir: str, the base directory to host all required sub-directories. - create_agent_fn: A function that takes as args a Tensorflow session and an - environment, and returns an agent. - create_environment_fn: A function which receives a problem name and - creates a Gym environment for that problem (e.g. an Atari 2600 game). - checkpoint_file_prefix: str, the prefix to use for checkpoint files. - logging_file_prefix: str, prefix to use for the log files. - log_every_n: int, the frequency for writing logs. - num_iterations: int, the iteration number threshold (must be greater than - start_iteration). - training_steps: int, the number of training steps to perform. - evaluation_steps: int, the number of evaluation steps to perform. - max_steps_per_episode: int, maximum number of steps after which an episode - terminates. - - This constructor will take the following actions: - - Initialize an environment. - - Initialize a `tf.Session`. - - Initialize a logger. - - Initialize an agent. - - Reload from the latest checkpoint, if available, and initialize the - Checkpointer object. - """ - assert base_dir is not None - self._logging_file_prefix = logging_file_prefix - self._log_every_n = log_every_n - self._num_iterations = num_iterations - self._runtype = runtype - self._game = game - self._training_steps = training_steps - self._evaluation_steps = evaluation_steps - self._max_steps_per_episode = max_steps_per_episode - self._base_dir = base_dir - self._create_directories() - self._summary_writer = tf.summary.FileWriter(self._base_dir) - self.testing = False - - if 'sticky' in runtype: - self.sticky_actions = True - else: - self.sticky_actions = False - self._environment = create_environment_fn(sticky_actions=self.sticky_actions) - # Set up a session and initialize variables. - self._sess = tf.Session('', config=tf.ConfigProto(allow_soft_placement=True, gpu_options=tf.GPUOptions(allow_growth=True))) - self._agent = create_agent_fn(self._sess, self._environment, - summary_writer=self._summary_writer) - self._agent.testing = self.testing - self._summary_writer.add_graph(graph=tf.get_default_graph()) - self._sess.run(tf.global_variables_initializer()) - - self._initialize_checkpointer_and_maybe_resume(checkpoint_file_prefix) - - self.step_count_total = 0 - #game = self._environment.name - print (">>>>>>>>>", self._game) - self.date = date = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") - self.job_name = self._game + '-' + self._runtype + '-' + date - self.filename = './fout-results/results-%s_%s-%s.txt' % (self._game, self._runtype, date) - self.filename_test = './fout-results/testresults-%s_%s-%s.txt' % (self._game, self._runtype, date) - self.filename_test_raw = 'testresults-%s_%s-%s.txt' % (self._game, self._runtype, date) - if not self.testing: - self.fout = open(self.filename, 'w+') - self.fout_test = open(self.filename_test, 'w+') - - def _create_directories(self): - """Create necessary sub-directories.""" - self._checkpoint_dir = os.path.join(self._base_dir, 'checkpoints') - self._logger = logger.Logger(os.path.join(self._base_dir, 'logs')) - - def _initialize_checkpointer_and_maybe_resume(self, checkpoint_file_prefix): - """Reloads the latest checkpoint if it exists. - - This method will first create a `Checkpointer` object and then call - `checkpointer.get_latest_checkpoint_number` to determine if there is a valid - checkpoint in self._checkpoint_dir, and what the largest file number is. - If a valid checkpoint file is found, it will load the bundled data from this - file and will pass it to the agent for it to reload its data. - If the agent is able to successfully unbundle, this method will verify that - the unbundled data contains the keys,'logs' and 'current_iteration'. It will - then load the `Logger`'s data from the bundle, and will return the iteration - number keyed by 'current_iteration' as one of the return values (along with - the `Checkpointer` object). - - Args: - checkpoint_file_prefix: str, the checkpoint file prefix. - - Returns: - start_iteration: int, the iteration number to start the experiment from. - experiment_checkpointer: `Checkpointer` object for the experiment. - """ - self._checkpointer = checkpointer.Checkpointer(self._checkpoint_dir, - checkpoint_file_prefix) - self._start_iteration = 0 - # Check if checkpoint exists. Note that the existence of checkpoint 0 means - # that we have finished iteration 0 (so we will start from iteration 1). - latest_checkpoint_version = checkpointer.get_latest_checkpoint_number( - self._checkpoint_dir) - if latest_checkpoint_version >= 0 and self.testing: - experiment_data = self._checkpointer.load_checkpoint( - latest_checkpoint_version) - if self._agent.unbundle( - self._checkpoint_dir, latest_checkpoint_version, experiment_data): - assert 'logs' in experiment_data - assert 'current_iteration' in experiment_data - self._logger.data = experiment_data['logs'] - self._start_iteration = experiment_data['current_iteration'] + 1 - tf.logging.info('Reloaded checkpoint and will start from iteration %d', - self._start_iteration) - #self.testing = True - #self._agent.testing = True - print ('TESTING:', self.testing) - - def _initialize_episode(self, run_mode_str): - """Initialization for a new episode. - - Returns: - action: int, the initial action chosen by the agent. - """ - initial_observation = self._environment.reset() - if not self.testing: - if run_mode_str == 'eval' and not self.sticky_actions: - noops = np.random.randint(1, 31) - for _ in range(noops): - #print ('noop') - initial_observation, _, done, _ = self._environment.step(0) - if done: - initial_observation = self._environment.reset() - return self._agent.begin_episode(initial_observation) - - def _run_one_step(self, action): - """Executes a single step in the environment. - - Args: - action: int, the action to perform in the environment. - - Returns: - The observation, reward, and is_terminal values returned from the - environment. - """ - observation, reward, is_terminal, _ = self._environment.step(action) - ''' - if self.testing: - image = self._environment.render('rgb_array') - self._agent.vis['s'].append(image) - self._agent.vis['state_input'].append(image) - #print (image.shape) - self._agent.vis['r'].append(reward) - quantiles, values = self._sess.run([ - self._agent._net_outputs.quantiles, - self._agent._net_outputs.quantile_values], - {self._agent.state_ph: self._agent.state} - ) - self._agent.vis['fraction'].append(quantiles) - self._agent.vis['values'].append(values) - #print (quantiles.shape) - ''' - return observation, reward, is_terminal - - def _end_episode(self, reward): - """Finalizes an episode run. - - Args: - reward: float, the last reward from the environment. - """ - self._agent.end_episode(reward) - - def _run_one_episode(self, run_mode_str): - """Executes a full trajectory of the agent interacting with the environment. - - Returns: - The number of steps taken and the total reward. - """ - step_number = 0 - total_reward = 0. - - action = self._initialize_episode(run_mode_str) - is_terminal = False - - # Keep interacting until we reach a terminal state. - while True: - observation, reward, is_terminal = self._run_one_step(action) - - total_reward += reward - step_number += 1 - - # Perform reward clipping. - reward = np.clip(reward, -1, 1) - - if (self._environment.game_over or - step_number == self._max_steps_per_episode): - # Stop the run loop once we reach the true end of episode. - break - elif is_terminal: - # If we lose a life but the episode is not over, signal an artificial - # end of episode to the agent. - self._agent.end_episode(reward) - action = self._agent.begin_episode(observation) - else: - action = self._agent.step(reward, observation) - - self._end_episode(reward) - - return step_number, total_reward - - def _run_one_phase(self, min_steps, statistics, run_mode_str): - """Runs the agent/environment loop until a desired number of steps. - - We follow the Machado et al., 2017 convention of running full episodes, - and terminating once we've run a minimum number of steps. - - Args: - min_steps: int, minimum number of steps to generate in this phase. - statistics: `IterationStatistics` object which records the experimental - results. - run_mode_str: str, describes the run mode for this agent. - - Returns: - Tuple containing the number of steps taken in this phase (int), the sum of - returns (float), and the number of episodes performed (int). - """ - step_count = 0 - num_episodes = 0 - sum_returns = 0. - - while step_count < min_steps: - start_time = time.time() - episode_length, episode_return = self._run_one_episode(run_mode_str) - time_delta = time.time() - start_time - statistics.append({ - '{}_episode_lengths'.format(run_mode_str): episode_length, - '{}_episode_returns'.format(run_mode_str): episode_return - }) - step_count += episode_length - self.step_count_total += episode_length - sum_returns += episode_return - num_episodes += 1 - # We use sys.stdout.write instead of tf.logging so as to flush frequently - # without generating a line break. - if not self.testing: - self.fout.write('%d %f %d\n' % (self.step_count_total, episode_return, episode_length)) - self.fout.flush() - sys.stdout.write('Steps executed: {} '.format(step_count) + - 'Episode length: {} '.format(episode_length) + - 'Return: {} '.format(episode_return) + - 'Average time one episode: {}\r'.format(episode_length/time_delta)) - sys.stdout.flush() - return step_count, sum_returns, num_episodes - - def _run_train_phase(self, statistics): - """Run training phase. - - Args: - statistics: `IterationStatistics` object which records the experimental - results. Note - This object is modified by this method. - - Returns: - num_episodes: int, The number of episodes run in this phase. - average_reward: The average reward generated in this phase. - """ - # Perform the training phase, during which the agent learns. - self._agent.eval_mode = False - start_time = time.time() - number_steps, sum_returns, num_episodes = self._run_one_phase( - self._training_steps, statistics, 'train') - average_return = sum_returns / num_episodes if num_episodes > 0 else 0.0 - statistics.append({'train_average_return': average_return}) - time_delta = time.time() - start_time - tf.logging.info('Average undiscounted return per training episode: %.2f', - average_return) - tf.logging.info('Average training steps per second: %.2f', - number_steps / time_delta) - return num_episodes, average_return - - def _run_eval_phase(self, statistics): - """Run evaluation phase. - - Args: - statistics: `IterationStatistics` object which records the experimental - results. Note - This object is modified by this method. - - Returns: - num_episodes: int, The number of episodes run in this phase. - average_reward: float, The average reward generated in this phase. - """ - # Perform the evaluation phase -- no learning. - self._agent.eval_mode = True - _, sum_returns, num_episodes = self._run_one_phase( - self._evaluation_steps, statistics, 'eval') - average_return = sum_returns / num_episodes if num_episodes > 0 else 0.0 - tf.logging.info('Average undiscounted return per evaluation episode: %.2f', - average_return) - statistics.append({'eval_average_return': average_return}) - return num_episodes, average_return - - def _run_one_iteration(self, iteration): - """Runs one iteration of agent/environment interaction. - - An iteration involves running several episodes until a certain number of - steps are obtained. The interleaving of train/eval phases implemented here - are to match the implementation of (Mnih et al., 2015). - - Args: - iteration: int, current iteration number, used as a global_step for saving - Tensorboard summaries. - - Returns: - A dict containing summary statistics for this iteration. - """ - statistics = iteration_statistics.IterationStatistics() - tf.logging.info('Starting iteration %d', iteration) - num_episodes_train, average_reward_train = self._run_train_phase( - statistics) - num_episodes_eval, average_reward_eval = self._run_eval_phase( - statistics) - - self._save_tensorboard_summaries(iteration, num_episodes_train, - average_reward_train, num_episodes_eval, - average_reward_eval) - return statistics.data_lists - - def _save_tensorboard_summaries(self, iteration, - num_episodes_train, - average_reward_train, - num_episodes_eval, - average_reward_eval): - """Save statistics as tensorboard summaries. - - Args: - iteration: int, The current iteration number. - num_episodes_train: int, number of training episodes run. - average_reward_train: float, The average training reward. - num_episodes_eval: int, number of evaluation episodes run. - average_reward_eval: float, The average evaluation reward. - """ - summary = tf.Summary(value=[ - tf.Summary.Value(tag='Train/NumEpisodes', - simple_value=num_episodes_train), - tf.Summary.Value(tag='Train/AverageReturns', - simple_value=average_reward_train), - tf.Summary.Value(tag='Eval/NumEpisodes', - simple_value=num_episodes_eval), - tf.Summary.Value(tag='Eval/AverageReturns', - simple_value=average_reward_eval) - ]) - self._summary_writer.add_summary(summary, iteration) - - def _log_experiment(self, iteration, statistics): - """Records the results of the current iteration. - - Args: - iteration: int, iteration number. - statistics: `IterationStatistics` object containing statistics to log. - """ - self._logger['iteration_{:d}'.format(iteration)] = statistics - if iteration % self._log_every_n == 0: - self._logger.log_to_file(self._logging_file_prefix, iteration) - - def _checkpoint_experiment(self, iteration): - """Checkpoint experiment data. - - Args: - iteration: int, iteration number for checkpointing. - """ - experiment_data = self._agent.bundle_and_checkpoint(self._checkpoint_dir, - iteration) - if experiment_data: - experiment_data['current_iteration'] = iteration - experiment_data['logs'] = self._logger.data - self._checkpointer.save_checkpoint(iteration, experiment_data) - - def run_experiment(self): - """Runs a full experiment, spread over multiple iterations.""" - tf.logging.info('Beginning training...') - if self._num_iterations <= self._start_iteration: - tf.logging.warning('num_iterations (%d) < start_iteration(%d)', - self._num_iterations, self._start_iteration) - return - - for iteration in range(self._start_iteration, self._num_iterations): - statistics = self._run_one_iteration(iteration) - self._log_experiment(iteration, statistics) - self._checkpoint_experiment(iteration) - - -@gin.configurable -class TrainRunner(Runner): - """Object that handles running experiments. - - The `TrainRunner` differs from the base `Runner` class in that it does not - the evaluation phase. Checkpointing and logging for the train phase are - preserved as before. - """ - - def __init__(self, base_dir, create_agent_fn, - create_environment_fn=atari_lib.create_atari_environment): - """Initialize the TrainRunner object in charge of running a full experiment. - - Args: - base_dir: str, the base directory to host all required sub-directories. - create_agent_fn: A function that takes as args a Tensorflow session and an - environment, and returns an agent. - create_environment_fn: A function which receives a problem name and - creates a Gym environment for that problem (e.g. an Atari 2600 game). - """ - tf.logging.info('Creating TrainRunner ...') - super(TrainRunner, self).__init__(base_dir, create_agent_fn, - create_environment_fn) - self._agent.eval_mode = False - - def _run_one_iteration(self, iteration): - """Runs one iteration of agent/environment interaction. - - An iteration involves running several episodes until a certain number of - steps are obtained. This method differs from the `_run_one_iteration` method - in the base `Runner` class in that it only runs the train phase. - - Args: - iteration: int, current iteration number, used as a global_step for saving - Tensorboard summaries. - - Returns: - A dict containing summary statistics for this iteration. - """ - statistics = iteration_statistics.IterationStatistics() - num_episodes_train, average_reward_train = self._run_train_phase( - statistics) - - self._save_tensorboard_summaries(iteration, num_episodes_train, - average_reward_train) - return statistics.data_lists - - def _save_tensorboard_summaries(self, iteration, num_episodes, - average_reward): - """Save statistics as tensorboard summaries.""" - summary = tf.Summary(value=[ - tf.Summary.Value(tag='Train/NumEpisodes', simple_value=num_episodes), - tf.Summary.Value( - tag='Train/AverageReturns', simple_value=average_reward), - ]) - self._summary_writer.add_summary(summary, iteration) +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Module defining classes and helper methods for general agents.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import time +import datetime +import csv, json, pickle + +from dopamine.agents.dqn import dqn_agent +from dopamine.agents.implicit_quantile import implicit_quantile_agent +from dopamine.agents.rainbow import rainbow_agent +from dopamine.discrete_domains import atari_lib +from dopamine.discrete_domains import checkpointer +from dopamine.discrete_domains import iteration_statistics +from dopamine.discrete_domains import logger + +import numpy as np +import tensorflow as tf + +import gin.tf + + +def load_gin_configs(gin_files, gin_bindings): + """Loads gin configuration files. + + Args: + gin_files: list, of paths to the gin configuration files for this + experiment. + gin_bindings: list, of gin parameter bindings to override the values in + the config files. + """ + gin.parse_config_files_and_bindings(gin_files, + bindings=gin_bindings, + skip_unknown=False) + + +@gin.configurable +def create_agent(sess, environment, agent_name=None, summary_writer=None, + debug_mode=False): + """Creates an agent. + + Args: + sess: A `tf.Session` object for running associated ops. + environment: A gym environment (e.g. Atari 2600). + agent_name: str, name of the agent to create. + summary_writer: A Tensorflow summary writer to pass to the agent + for in-agent training statistics in Tensorboard. + debug_mode: bool, whether to output Tensorboard summaries. If set to true, + the agent will output in-episode statistics to Tensorboard. Disabled by + default as this results in slower training. + + Returns: + agent: An RL agent. + + Raises: + ValueError: If `agent_name` is not in supported list. + """ + assert agent_name is not None + if not debug_mode: + summary_writer = None + if agent_name == 'dqn': + return dqn_agent.DQNAgent(sess, num_actions=environment.action_space.n, + summary_writer=summary_writer) + elif agent_name == 'rainbow': + return rainbow_agent.RainbowAgent( + sess, num_actions=environment.action_space.n, + summary_writer=summary_writer) + elif agent_name == 'implicit_quantile': + return implicit_quantile_agent.ImplicitQuantileAgent( + sess, num_actions=environment.action_space.n, + summary_writer=summary_writer) + else: + raise ValueError('Unknown agent: {}'.format(agent_name)) + + +@gin.configurable +def create_runner(base_dir, schedule='continuous_train_and_eval'): + """Creates an experiment Runner. + + Args: + base_dir: str, base directory for hosting all subdirectories. + schedule: string, which type of Runner to use. + + Returns: + runner: A `Runner` like object. + + Raises: + ValueError: When an unknown schedule is encountered. + """ + assert base_dir is not None + # Continuously runs training and evaluation until max num_iterations is hit. + if schedule == 'continuous_train_and_eval': + return Runner(base_dir, create_agent) + # Continuously runs training until max num_iterations is hit. + elif schedule == 'continuous_train': + return TrainRunner(base_dir, create_agent) + else: + raise ValueError('Unknown schedule: {}'.format(schedule)) + + +@gin.configurable +class Runner(object): + """Object that handles running Dopamine experiments. + + Here we use the term 'experiment' to mean simulating interactions between the + agent and the environment and reporting some statistics pertaining to these + interactions. + + A simple scenario to train a DQN agent is as follows: + + ```python + import dopamine.discrete_domains.atari_lib + base_dir = '/tmp/simple_example' + def create_agent(sess, environment): + return dqn_agent.DQNAgent(sess, num_actions=environment.action_space.n) + runner = Runner(base_dir, create_agent, atari_lib.create_atari_environment) + runner.run() + ``` + """ + + def __init__(self, + base_dir, + create_agent_fn, + create_environment_fn=atari_lib.create_atari_environment, + checkpoint_file_prefix='ckpt', + logging_file_prefix='log', + log_every_n=1, + num_iterations=200, + runtype='run', + game='Pong', + training_steps=250000, + evaluation_steps=125000, + max_steps_per_episode=27000): + """Initialize the Runner object in charge of running a full experiment. + + Args: + base_dir: str, the base directory to host all required sub-directories. + create_agent_fn: A function that takes as args a Tensorflow session and an + environment, and returns an agent. + create_environment_fn: A function which receives a problem name and + creates a Gym environment for that problem (e.g. an Atari 2600 game). + checkpoint_file_prefix: str, the prefix to use for checkpoint files. + logging_file_prefix: str, prefix to use for the log files. + log_every_n: int, the frequency for writing logs. + num_iterations: int, the iteration number threshold (must be greater than + start_iteration). + training_steps: int, the number of training steps to perform. + evaluation_steps: int, the number of evaluation steps to perform. + max_steps_per_episode: int, maximum number of steps after which an episode + terminates. + + This constructor will take the following actions: + - Initialize an environment. + - Initialize a `tf.Session`. + - Initialize a logger. + - Initialize an agent. + - Reload from the latest checkpoint, if available, and initialize the + Checkpointer object. + """ + assert base_dir is not None + self._logging_file_prefix = logging_file_prefix + self._log_every_n = log_every_n + self._num_iterations = num_iterations + self._runtype = runtype + self._game = game + self._training_steps = training_steps + self._evaluation_steps = evaluation_steps + self._max_steps_per_episode = max_steps_per_episode + self._base_dir = base_dir + self._create_directories() + self._summary_writer = tf.summary.FileWriter(self._base_dir) + self.testing = False + + if 'sticky' in runtype: + self.sticky_actions = True + else: + self.sticky_actions = False + self._environment = create_environment_fn(sticky_actions=self.sticky_actions) + # Set up a session and initialize variables. + self._sess = tf.Session('', config=tf.ConfigProto(allow_soft_placement=True, gpu_options=tf.GPUOptions(allow_growth=True))) + self._agent = create_agent_fn(self._sess, self._environment, + summary_writer=self._summary_writer) + self._agent.testing = self.testing + self._summary_writer.add_graph(graph=tf.get_default_graph()) + self._sess.run(tf.global_variables_initializer()) + + self._initialize_checkpointer_and_maybe_resume(checkpoint_file_prefix) + + self.step_count_total = 0 + #game = self._environment.name + print (">>>>>>>>>", self._game) + self.date = date = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + self.job_name = self._game + '-' + self._runtype + '-' + date + self.filename = './fout-results/results-%s_%s-%s.txt' % (self._game, self._runtype, date) + self.filename_test = './fout-results/testresults-%s_%s-%s.txt' % (self._game, self._runtype, date) + self.filename_test_raw = 'testresults-%s_%s-%s.txt' % (self._game, self._runtype, date) + if not self.testing: + self.fout = open(self.filename, 'w+') + self.fout_test = open(self.filename_test, 'w+') + + def _create_directories(self): + """Create necessary sub-directories.""" + self._checkpoint_dir = os.path.join(self._base_dir, 'checkpoints') + self._logger = logger.Logger(os.path.join(self._base_dir, 'logs')) + + def _initialize_checkpointer_and_maybe_resume(self, checkpoint_file_prefix): + """Reloads the latest checkpoint if it exists. + + This method will first create a `Checkpointer` object and then call + `checkpointer.get_latest_checkpoint_number` to determine if there is a valid + checkpoint in self._checkpoint_dir, and what the largest file number is. + If a valid checkpoint file is found, it will load the bundled data from this + file and will pass it to the agent for it to reload its data. + If the agent is able to successfully unbundle, this method will verify that + the unbundled data contains the keys,'logs' and 'current_iteration'. It will + then load the `Logger`'s data from the bundle, and will return the iteration + number keyed by 'current_iteration' as one of the return values (along with + the `Checkpointer` object). + + Args: + checkpoint_file_prefix: str, the checkpoint file prefix. + + Returns: + start_iteration: int, the iteration number to start the experiment from. + experiment_checkpointer: `Checkpointer` object for the experiment. + """ + self._checkpointer = checkpointer.Checkpointer(self._checkpoint_dir, + checkpoint_file_prefix) + self._start_iteration = 0 + # Check if checkpoint exists. Note that the existence of checkpoint 0 means + # that we have finished iteration 0 (so we will start from iteration 1). + latest_checkpoint_version = checkpointer.get_latest_checkpoint_number( + self._checkpoint_dir) + if latest_checkpoint_version >= 0 and self.testing: + experiment_data = self._checkpointer.load_checkpoint( + latest_checkpoint_version) + if self._agent.unbundle( + self._checkpoint_dir, latest_checkpoint_version, experiment_data): + assert 'logs' in experiment_data + assert 'current_iteration' in experiment_data + self._logger.data = experiment_data['logs'] + self._start_iteration = experiment_data['current_iteration'] + 1 + tf.logging.info('Reloaded checkpoint and will start from iteration %d', + self._start_iteration) + #self.testing = True + #self._agent.testing = True + print ('TESTING:', self.testing) + + def _initialize_episode(self, run_mode_str): + """Initialization for a new episode. + + Returns: + action: int, the initial action chosen by the agent. + """ + initial_observation = self._environment.reset() + if not self.testing: + if run_mode_str == 'eval' and not self.sticky_actions: + noops = np.random.randint(1, 31) + for _ in range(noops): + #print ('noop') + initial_observation, _, done, _ = self._environment.step(0) + if done: + initial_observation = self._environment.reset() + return self._agent.begin_episode(initial_observation) + + def _run_one_step(self, action): + """Executes a single step in the environment. + + Args: + action: int, the action to perform in the environment. + + Returns: + The observation, reward, and is_terminal values returned from the + environment. + """ + observation, reward, is_terminal, _ = self._environment.step(action) + ''' + if self.testing: + image = self._environment.render('rgb_array') + self._agent.vis['s'].append(image) + self._agent.vis['state_input'].append(image) + #print (image.shape) + self._agent.vis['r'].append(reward) + quantiles, values = self._sess.run([ + self._agent._net_outputs.quantiles, + self._agent._net_outputs.quantile_values], + {self._agent.state_ph: self._agent.state} + ) + self._agent.vis['fraction'].append(quantiles) + self._agent.vis['values'].append(values) + #print (quantiles.shape) + ''' + return observation, reward, is_terminal + + def _end_episode(self, reward): + """Finalizes an episode run. + + Args: + reward: float, the last reward from the environment. + """ + self._agent.end_episode(reward) + + def _run_one_episode(self, run_mode_str): + """Executes a full trajectory of the agent interacting with the environment. + + Returns: + The number of steps taken and the total reward. + """ + step_number = 0 + total_reward = 0. + + action = self._initialize_episode(run_mode_str) + is_terminal = False + + # Keep interacting until we reach a terminal state. + while True: + observation, reward, is_terminal = self._run_one_step(action) + + total_reward += reward + step_number += 1 + + # Perform reward clipping. + reward = np.clip(reward, -1, 1) + + if (self._environment.game_over or + step_number == self._max_steps_per_episode): + # Stop the run loop once we reach the true end of episode. + break + elif is_terminal: + # If we lose a life but the episode is not over, signal an artificial + # end of episode to the agent. + self._agent.end_episode(reward) + action = self._agent.begin_episode(observation) + else: + action = self._agent.step(reward, observation) + + self._end_episode(reward) + + return step_number, total_reward + + def _run_one_phase(self, min_steps, statistics, run_mode_str): + """Runs the agent/environment loop until a desired number of steps. + + We follow the Machado et al., 2017 convention of running full episodes, + and terminating once we've run a minimum number of steps. + + Args: + min_steps: int, minimum number of steps to generate in this phase. + statistics: `IterationStatistics` object which records the experimental + results. + run_mode_str: str, describes the run mode for this agent. + + Returns: + Tuple containing the number of steps taken in this phase (int), the sum of + returns (float), and the number of episodes performed (int). + """ + step_count = 0 + num_episodes = 0 + sum_returns = 0. + + while step_count < min_steps: + start_time = time.time() + episode_length, episode_return = self._run_one_episode(run_mode_str) + time_delta = time.time() - start_time + statistics.append({ + '{}_episode_lengths'.format(run_mode_str): episode_length, + '{}_episode_returns'.format(run_mode_str): episode_return + }) + step_count += episode_length + self.step_count_total += episode_length + sum_returns += episode_return + num_episodes += 1 + # We use sys.stdout.write instead of tf.logging so as to flush frequently + # without generating a line break. + if not self.testing: + self.fout.write('%d %f %d\n' % (self.step_count_total, episode_return, episode_length)) + self.fout.flush() + sys.stdout.write('Steps executed: {} '.format(step_count) + + 'Episode length: {} '.format(episode_length) + + 'Return: {} '.format(episode_return) + + 'Average time one episode: {}\r'.format(episode_length/time_delta)) + sys.stdout.flush() + return step_count, sum_returns, num_episodes + + def _run_train_phase(self, statistics): + """Run training phase. + + Args: + statistics: `IterationStatistics` object which records the experimental + results. Note - This object is modified by this method. + + Returns: + num_episodes: int, The number of episodes run in this phase. + average_reward: The average reward generated in this phase. + """ + # Perform the training phase, during which the agent learns. + self._agent.eval_mode = False + start_time = time.time() + number_steps, sum_returns, num_episodes = self._run_one_phase( + self._training_steps, statistics, 'train') + average_return = sum_returns / num_episodes if num_episodes > 0 else 0.0 + statistics.append({'train_average_return': average_return}) + time_delta = time.time() - start_time + tf.logging.info('Average undiscounted return per training episode: %.2f', + average_return) + tf.logging.info('Average training steps per second: %.2f', + number_steps / time_delta) + return num_episodes, average_return + + def _run_eval_phase(self, statistics): + """Run evaluation phase. + + Args: + statistics: `IterationStatistics` object which records the experimental + results. Note - This object is modified by this method. + + Returns: + num_episodes: int, The number of episodes run in this phase. + average_reward: float, The average reward generated in this phase. + """ + # Perform the evaluation phase -- no learning. + self._agent.eval_mode = True + _, sum_returns, num_episodes = self._run_one_phase( + self._evaluation_steps, statistics, 'eval') + average_return = sum_returns / num_episodes if num_episodes > 0 else 0.0 + tf.logging.info('Average undiscounted return per evaluation episode: %.2f', + average_return) + statistics.append({'eval_average_return': average_return}) + return num_episodes, average_return + + def _run_one_iteration(self, iteration): + """Runs one iteration of agent/environment interaction. + + An iteration involves running several episodes until a certain number of + steps are obtained. The interleaving of train/eval phases implemented here + are to match the implementation of (Mnih et al., 2015). + + Args: + iteration: int, current iteration number, used as a global_step for saving + Tensorboard summaries. + + Returns: + A dict containing summary statistics for this iteration. + """ + statistics = iteration_statistics.IterationStatistics() + tf.logging.info('Starting iteration %d', iteration) + num_episodes_train, average_reward_train = self._run_train_phase( + statistics) + num_episodes_eval, average_reward_eval = self._run_eval_phase( + statistics) + + self._save_tensorboard_summaries(iteration, num_episodes_train, + average_reward_train, num_episodes_eval, + average_reward_eval) + return statistics.data_lists + + def _save_tensorboard_summaries(self, iteration, + num_episodes_train, + average_reward_train, + num_episodes_eval, + average_reward_eval): + """Save statistics as tensorboard summaries. + + Args: + iteration: int, The current iteration number. + num_episodes_train: int, number of training episodes run. + average_reward_train: float, The average training reward. + num_episodes_eval: int, number of evaluation episodes run. + average_reward_eval: float, The average evaluation reward. + """ + summary = tf.Summary(value=[ + tf.Summary.Value(tag='Train/NumEpisodes', + simple_value=num_episodes_train), + tf.Summary.Value(tag='Train/AverageReturns', + simple_value=average_reward_train), + tf.Summary.Value(tag='Eval/NumEpisodes', + simple_value=num_episodes_eval), + tf.Summary.Value(tag='Eval/AverageReturns', + simple_value=average_reward_eval) + ]) + self._summary_writer.add_summary(summary, iteration) + + def _log_experiment(self, iteration, statistics): + """Records the results of the current iteration. + + Args: + iteration: int, iteration number. + statistics: `IterationStatistics` object containing statistics to log. + """ + self._logger['iteration_{:d}'.format(iteration)] = statistics + if iteration % self._log_every_n == 0: + self._logger.log_to_file(self._logging_file_prefix, iteration) + + def _checkpoint_experiment(self, iteration): + """Checkpoint experiment data. + + Args: + iteration: int, iteration number for checkpointing. + """ + experiment_data = self._agent.bundle_and_checkpoint(self._checkpoint_dir, + iteration) + if experiment_data: + experiment_data['current_iteration'] = iteration + experiment_data['logs'] = self._logger.data + self._checkpointer.save_checkpoint(iteration, experiment_data) + + def run_experiment(self): + """Runs a full experiment, spread over multiple iterations.""" + tf.logging.info('Beginning training...') + if self._num_iterations <= self._start_iteration: + tf.logging.warning('num_iterations (%d) < start_iteration(%d)', + self._num_iterations, self._start_iteration) + return + + for iteration in range(self._start_iteration, self._num_iterations): + statistics = self._run_one_iteration(iteration) + self._log_experiment(iteration, statistics) + self._checkpoint_experiment(iteration) + + +@gin.configurable +class TrainRunner(Runner): + """Object that handles running experiments. + + The `TrainRunner` differs from the base `Runner` class in that it does not + the evaluation phase. Checkpointing and logging for the train phase are + preserved as before. + """ + + def __init__(self, base_dir, create_agent_fn, + create_environment_fn=atari_lib.create_atari_environment): + """Initialize the TrainRunner object in charge of running a full experiment. + + Args: + base_dir: str, the base directory to host all required sub-directories. + create_agent_fn: A function that takes as args a Tensorflow session and an + environment, and returns an agent. + create_environment_fn: A function which receives a problem name and + creates a Gym environment for that problem (e.g. an Atari 2600 game). + """ + tf.logging.info('Creating TrainRunner ...') + super(TrainRunner, self).__init__(base_dir, create_agent_fn, + create_environment_fn) + self._agent.eval_mode = False + + def _run_one_iteration(self, iteration): + """Runs one iteration of agent/environment interaction. + + An iteration involves running several episodes until a certain number of + steps are obtained. This method differs from the `_run_one_iteration` method + in the base `Runner` class in that it only runs the train phase. + + Args: + iteration: int, current iteration number, used as a global_step for saving + Tensorboard summaries. + + Returns: + A dict containing summary statistics for this iteration. + """ + statistics = iteration_statistics.IterationStatistics() + num_episodes_train, average_reward_train = self._run_train_phase( + statistics) + + self._save_tensorboard_summaries(iteration, num_episodes_train, + average_reward_train) + return statistics.data_lists + + def _save_tensorboard_summaries(self, iteration, num_episodes, + average_reward): + """Save statistics as tensorboard summaries.""" + summary = tf.Summary(value=[ + tf.Summary.Value(tag='Train/NumEpisodes', simple_value=num_episodes), + tf.Summary.Value( + tag='Train/AverageReturns', simple_value=average_reward), + ]) + self._summary_writer.add_summary(summary, iteration) diff --git a/dopamine/discrete_domains/train.py b/dopamine/discrete_domains/train.py index 41ca762..60fe3e3 100644 --- a/dopamine/discrete_domains/train.py +++ b/dopamine/discrete_domains/train.py @@ -1,65 +1,65 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -r"""The entry point for running a Dopamine agent. - -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import sys -sys.path = ['../../'] + sys.path -print (sys.path) -#exit(0) - - - -from absl import app -from absl import flags - -from dopamine.discrete_domains import run_experiment - -import tensorflow as tf - - -flags.DEFINE_string('base_dir', None, - 'Base directory to host all required sub-directories.') -flags.DEFINE_multi_string( - 'gin_files', [], 'List of paths to gin configuration files (e.g.' - '"dopamine/agents/dqn/dqn.gin").') -flags.DEFINE_multi_string( - 'gin_bindings', [], - 'Gin bindings to override the values set in the config files ' - '(e.g. "DQNAgent.epsilon_train=0.1",' - ' "create_environment.game_name="Pong"").') - -FLAGS = flags.FLAGS - - -def main(unused_argv): - """Main method. - - Args: - unused_argv: Arguments (unused). - """ - tf.logging.set_verbosity(tf.logging.INFO) - run_experiment.load_gin_configs(FLAGS.gin_files, FLAGS.gin_bindings) - runner = run_experiment.create_runner(FLAGS.base_dir) - runner.run_experiment() - - -if __name__ == '__main__': - flags.mark_flag_as_required('base_dir') - app.run(main) +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +r"""The entry point for running a Dopamine agent. + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import sys +sys.path = ['../../'] + sys.path +print (sys.path) +#exit(0) + + + +from absl import app +from absl import flags + +from dopamine.discrete_domains import run_experiment + +import tensorflow as tf + + +flags.DEFINE_string('base_dir', None, + 'Base directory to host all required sub-directories.') +flags.DEFINE_multi_string( + 'gin_files', [], 'List of paths to gin configuration files (e.g.' + '"dopamine/agents/dqn/dqn.gin").') +flags.DEFINE_multi_string( + 'gin_bindings', [], + 'Gin bindings to override the values set in the config files ' + '(e.g. "DQNAgent.epsilon_train=0.1",' + ' "create_environment.game_name="Pong"").') + +FLAGS = flags.FLAGS + + +def main(unused_argv): + """Main method. + + Args: + unused_argv: Arguments (unused). + """ + tf.logging.set_verbosity(tf.logging.INFO) + run_experiment.load_gin_configs(FLAGS.gin_files, FLAGS.gin_bindings) + runner = run_experiment.create_runner(FLAGS.base_dir) + runner.run_experiment() + + +if __name__ == '__main__': + flags.mark_flag_as_required('base_dir') + app.run(main) diff --git a/dopamine/replay_memory/__init__.py b/dopamine/replay_memory/__init__.py index 920cbb5..dc108ba 100644 --- a/dopamine/replay_memory/__init__.py +++ b/dopamine/replay_memory/__init__.py @@ -1,15 +1,15 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/dopamine/replay_memory/circular_replay_buffer.py b/dopamine/replay_memory/circular_replay_buffer.py index 8c0d3f6..d92f92f 100644 --- a/dopamine/replay_memory/circular_replay_buffer.py +++ b/dopamine/replay_memory/circular_replay_buffer.py @@ -1,885 +1,885 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""The standard DQN replay memory. - -This implementation is an out-of-graph replay memory + in-graph wrapper. It -supports vanilla n-step updates of the form typically found in the literature, -i.e. where rewards are accumulated for n steps and the intermediate trajectory -is not exposed to the agent. This does not allow, for example, performing -off-policy corrections. -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import gzip -import math -import os -import pickle - -import numpy as np -import tensorflow as tf - -import gin.tf - -# Defines a type describing part of the tuple returned by the replay -# memory. Each element of the tuple is a tensor of shape [batch, ...] where -# ... is defined the 'shape' field of ReplayElement. The tensor type is -# given by the 'type' field. The 'name' field is for convenience and ease of -# debugging. -ReplayElement = ( - collections.namedtuple('shape_type', ['name', 'shape', 'type'])) - -# A prefix that can not collide with variable names for checkpoint files. -STORE_FILENAME_PREFIX = '$store$_' - -# This constant determines how many iterations a checkpoint is kept for. -CHECKPOINT_DURATION = 4 -MAX_SAMPLE_ATTEMPTS = 1000 - - -def invalid_range(cursor, replay_capacity, stack_size, update_horizon): - """Returns a array with the indices of cursor-related invalid transitions. - - There are update_horizon + stack_size invalid indices: - - The update_horizon indices before the cursor, because we do not have a - valid N-step transition (including the next state). - - The stack_size indices on or immediately after the cursor. - If N = update_horizon, K = stack_size, and the cursor is at c, invalid - indices are: - c - N, c - N + 1, ..., c, c + 1, ..., c + K - 1. - - It handles special cases in a circular buffer in the beginning and the end. - - Args: - cursor: int, the position of the cursor. - replay_capacity: int, the size of the replay memory. - stack_size: int, the size of the stacks returned by the replay memory. - update_horizon: int, the agent's update horizon. - Returns: - np.array of size stack_size with the invalid indices. - """ - assert cursor < replay_capacity - return np.array( - [(cursor - update_horizon + i) % replay_capacity - for i in range(stack_size + update_horizon)]) - - -class OutOfGraphReplayBuffer(object): - """A simple out-of-graph Replay Buffer. - - Stores transitions, state, action, reward, next_state, terminal (and any - extra contents specified) in a circular buffer and provides a uniform - transition sampling function. - - When the states consist of stacks of observations storing the states is - inefficient. This class writes observations and constructs the stacked states - at sample time. - - Attributes: - add_count: int, counter of how many transitions have been added (including - the blank ones at the beginning of an episode). - invalid_range: np.array, an array with the indices of cursor-related invalid - transitions - """ - - def __init__(self, - observation_shape, - stack_size, - replay_capacity, - batch_size, - update_horizon=1, - gamma=0.99, - max_sample_attempts=MAX_SAMPLE_ATTEMPTS, - extra_storage_types=None, - observation_dtype=np.uint8, - action_shape=(), - action_dtype=np.int32, - reward_shape=(), - reward_dtype=np.float32): - """Initializes OutOfGraphReplayBuffer. - - Args: - observation_shape: tuple of ints. - stack_size: int, number of frames to use in state stack. - replay_capacity: int, number of transitions to keep in memory. - batch_size: int. - update_horizon: int, length of update ('n' in n-step update). - gamma: int, the discount factor. - max_sample_attempts: int, the maximum number of attempts allowed to - get a sample. - extra_storage_types: list of ReplayElements defining the type of the extra - contents that will be stored and returned by sample_transition_batch. - observation_dtype: np.dtype, type of the observations. Defaults to - np.uint8 for Atari 2600. - action_shape: tuple of ints, the shape for the action vector. Empty tuple - means the action is a scalar. - action_dtype: np.dtype, type of elements in the action. - reward_shape: tuple of ints, the shape of the reward vector. Empty tuple - means the reward is a scalar. - reward_dtype: np.dtype, type of elements in the reward. - - Raises: - ValueError: If replay_capacity is too small to hold at least one - transition. - """ - assert isinstance(observation_shape, tuple) - if replay_capacity < update_horizon + stack_size: - raise ValueError('There is not enough capacity to cover ' - 'update_horizon and stack_size.') - - tf.logging.info( - 'Creating a %s replay memory with the following parameters:', - self.__class__.__name__) - tf.logging.info('\t observation_shape: %s', str(observation_shape)) - tf.logging.info('\t observation_dtype: %s', str(observation_dtype)) - tf.logging.info('\t stack_size: %d', stack_size) - tf.logging.info('\t replay_capacity: %d', replay_capacity) - tf.logging.info('\t batch_size: %d', batch_size) - tf.logging.info('\t update_horizon: %d', update_horizon) - tf.logging.info('\t gamma: %f', gamma) - - self._action_shape = action_shape - self._action_dtype = action_dtype - self._reward_shape = reward_shape - self._reward_dtype = reward_dtype - self._observation_shape = observation_shape - self._stack_size = stack_size - self._state_shape = self._observation_shape + (self._stack_size,) - self._replay_capacity = replay_capacity - self._batch_size = batch_size - self._update_horizon = update_horizon - self._gamma = gamma - self._observation_dtype = observation_dtype - self._max_sample_attempts = max_sample_attempts - if extra_storage_types: - self._extra_storage_types = extra_storage_types - else: - self._extra_storage_types = [] - self._create_storage() - self.add_count = np.array(0) - self.invalid_range = np.zeros((self._stack_size)) - # When the horizon is > 1, we compute the sum of discounted rewards as a dot - # product using the precomputed vector . - self._cumulative_discount_vector = np.array( - [math.pow(self._gamma, n) for n in range(update_horizon)], - dtype=np.float32) - - def _create_storage(self): - """Creates the numpy arrays used to store transitions. - """ - self._store = {} - for storage_element in self.get_storage_signature(): - array_shape = [self._replay_capacity] + list(storage_element.shape) - self._store[storage_element.name] = np.empty( - array_shape, dtype=storage_element.type) - - def get_add_args_signature(self): - """The signature of the add function. - - Note - Derived classes may return a different signature. - - Returns: - list of ReplayElements defining the type of the argument signature needed - by the add function. - """ - return self.get_storage_signature() - - def get_storage_signature(self): - """Returns a default list of elements to be stored in this replay memory. - - Note - Derived classes may return a different signature. - - Returns: - list of ReplayElements defining the type of the contents stored. - """ - storage_elements = [ - ReplayElement('observation', self._observation_shape, - self._observation_dtype), - ReplayElement('action', self._action_shape, self._action_dtype), - ReplayElement('reward', self._reward_shape, self._reward_dtype), - ReplayElement('terminal', (), np.uint8) - ] - - for extra_replay_element in self._extra_storage_types: - storage_elements.append(extra_replay_element) - return storage_elements - - def _add_zero_transition(self): - """Adds a padding transition filled with zeros (Used in episode beginnings). - """ - zero_transition = [] - for element_type in self.get_add_args_signature(): - zero_transition.append( - np.zeros(element_type.shape, dtype=element_type.type)) - self._add(*zero_transition) - - def add(self, observation, action, reward, terminal, *args): - """Adds a transition to the replay memory. - - This function checks the types and handles the padding at the beginning of - an episode. Then it calls the _add function. - - Since the next_observation in the transition will be the observation added - next there is no need to pass it. - - If the replay memory is at capacity the oldest transition will be discarded. - - Args: - observation: np.array with shape observation_shape. - action: int, the action in the transition. - reward: float, the reward received in the transition. - terminal: A uint8 acting as a boolean indicating whether the transition - was terminal (1) or not (0). - *args: extra contents with shapes and dtypes according to - extra_storage_types. - """ - self._check_add_types(observation, action, reward, terminal, *args) - if self.is_empty() or self._store['terminal'][self.cursor() - 1] == 1: - for _ in range(self._stack_size - 1): - # Child classes can rely on the padding transitions being filled with - # zeros. This is useful when there is a priority argument. - self._add_zero_transition() - self._add(observation, action, reward, terminal, *args) - - def _add(self, *args): - """Internal add method to add to the storage arrays. - - Args: - *args: All the elements in a transition. - """ - cursor = self.cursor() - - arg_names = [e.name for e in self.get_add_args_signature()] - for arg_name, arg in zip(arg_names, args): - self._store[arg_name][cursor] = arg - - self.add_count += 1 - self.invalid_range = invalid_range( - self.cursor(), self._replay_capacity, self._stack_size, - self._update_horizon) - - def _check_add_types(self, *args): - """Checks if args passed to the add method match those of the storage. - - Args: - *args: Args whose types need to be validated. - - Raises: - ValueError: If args have wrong shape or dtype. - """ - if len(args) != len(self.get_add_args_signature()): - raise ValueError('Add expects {} elements, received {}'.format( - len(self.get_add_args_signature()), len(args))) - for arg_element, store_element in zip(args, self.get_add_args_signature()): - if isinstance(arg_element, np.ndarray): - arg_shape = arg_element.shape - elif isinstance(arg_element, tuple) or isinstance(arg_element, list): - # TODO(b/80536437). This is not efficient when arg_element is a list. - arg_shape = np.array(arg_element).shape - else: - # Assume it is scalar. - arg_shape = tuple() - store_element_shape = tuple(store_element.shape) - if arg_shape != store_element_shape: - raise ValueError('arg has shape {}, expected {}'.format( - arg_shape, store_element_shape)) - - def is_empty(self): - """Is the Replay Buffer empty?""" - return self.add_count == 0 - - def is_full(self): - """Is the Replay Buffer full?""" - return self.add_count >= self._replay_capacity - - def cursor(self): - """Index to the location where the next transition will be written.""" - return self.add_count % self._replay_capacity - - def get_range(self, array, start_index, end_index): - """Returns the range of array at the index handling wraparound if necessary. - - Args: - array: np.array, the array to get the stack from. - start_index: int, index to the start of the range to be returned. Range - will wraparound if start_index is smaller than 0. - end_index: int, exclusive end index. Range will wraparound if end_index - exceeds replay_capacity. - - Returns: - np.array, with shape [end_index - start_index, array.shape[1:]]. - """ - assert end_index > start_index, 'end_index must be larger than start_index' - assert end_index >= 0 - assert start_index < self._replay_capacity - if not self.is_full(): - assert end_index <= self.cursor(), ( - 'Index {} has not been added.'.format(start_index)) - - # Fast slice read when there is no wraparound. - if start_index % self._replay_capacity < end_index % self._replay_capacity: - return_array = array[start_index:end_index, ...] - # Slow list read. - else: - indices = [(start_index + i) % self._replay_capacity - for i in range(end_index - start_index)] - return_array = array[indices, ...] - return return_array - - def get_observation_stack(self, index): - return self._get_element_stack(index, 'observation') - - def _get_element_stack(self, index, element_name): - state = self.get_range(self._store[element_name], - index - self._stack_size + 1, index + 1) - # The stacking axis is 0 but the agent expects as the last axis. - return np.moveaxis(state, 0, -1) - - def get_terminal_stack(self, index): - return self.get_range(self._store['terminal'], index - self._stack_size + 1, - index + 1) - - def is_valid_transition(self, index): - """Checks if the index contains a valid transition. - - Checks for collisions with the end of episodes and the current position - of the cursor. - - Args: - index: int, the index to the state in the transition. - - Returns: - Is the index valid: Boolean. - - """ - # Check the index is in the valid range - if index < 0 or index >= self._replay_capacity: - return False - if not self.is_full(): - # The indices and next_indices must be smaller than the cursor. - if index >= self.cursor() - self._update_horizon: - return False - # The first few indices contain the padding states of the first episode. - if index < self._stack_size - 1: - return False - - # Skip transitions that straddle the cursor. - if index in set(self.invalid_range): - return False - - # If there are terminal flags in any other frame other than the last one - # the stack is not valid, so don't sample it. - if self.get_terminal_stack(index)[:-1].any(): - return False - - return True - - def _create_batch_arrays(self, batch_size): - """Create a tuple of arrays with the type of get_transition_elements. - - When using the WrappedReplayBuffer with staging enabled it is important to - create new arrays every sample because StaginArea keeps a pointer to the - returned arrays. - - Args: - batch_size: (int) number of transitions returned. If None the default - batch_size will be used. - - Returns: - Tuple of np.arrays with the shape and type of get_transition_elements. - """ - transition_elements = self.get_transition_elements(batch_size) - batch_arrays = [] - for element in transition_elements: - batch_arrays.append(np.empty(element.shape, dtype=element.type)) - return tuple(batch_arrays) - - def sample_index_batch(self, batch_size): - """Returns a batch of valid indices sampled uniformly. - - Args: - batch_size: int, number of indices returned. - - Returns: - list of ints, a batch of valid indices sampled uniformly. - - Raises: - RuntimeError: If the batch was not constructed after maximum number of - tries. - """ - if self.is_full(): - # add_count >= self._replay_capacity > self._stack_size - min_id = self.cursor() - self._replay_capacity + self._stack_size - 1 - max_id = self.cursor() - self._update_horizon - else: - # add_count < self._replay_capacity - min_id = self._stack_size - 1 - max_id = self.cursor() - self._update_horizon - if max_id <= min_id: - raise RuntimeError('Cannot sample a batch with fewer than stack size ' - '({}) + update_horizon ({}) transitions.'. - format(self._stack_size, self._update_horizon)) - - indices = [] - attempt_count = 0 - while (len(indices) < batch_size and - attempt_count < self._max_sample_attempts): - attempt_count += 1 - index = np.random.randint(min_id, max_id) % self._replay_capacity - if self.is_valid_transition(index): - indices.append(index) - if len(indices) != batch_size: - raise RuntimeError( - 'Max sample attempts: Tried {} times but only sampled {}' - ' valid indices. Batch size is {}'. - format(self._max_sample_attempts, len(indices), batch_size)) - - return indices - - def sample_transition_batch(self, batch_size=None, indices=None): - """Returns a batch of transitions (including any extra contents). - - If get_transition_elements has been overridden and defines elements not - stored in self._store, an empty array will be returned and it will be - left to the child class to fill it. For example, for the child class - OutOfGraphPrioritizedReplayBuffer, the contents of the - sampling_probabilities are stored separately in a sum tree. - - When the transition is terminal next_state_batch has undefined contents. - - NOTE: This transition contains the indices of the sampled elements. These - are only valid during the call to sample_transition_batch, i.e. they may - be used by subclasses of this replay buffer but may point to different data - as soon as sampling is done. - - Args: - batch_size: int, number of transitions returned. If None, the default - batch_size will be used. - indices: None or list of ints, the indices of every transition in the - batch. If None, sample the indices uniformly. - - Returns: - transition_batch: tuple of np.arrays with the shape and type as in - get_transition_elements(). - - Raises: - ValueError: If an element to be sampled is missing from the replay buffer. - """ - if batch_size is None: - batch_size = self._batch_size - if indices is None: - indices = self.sample_index_batch(batch_size) - assert len(indices) == batch_size - - transition_elements = self.get_transition_elements(batch_size) - batch_arrays = self._create_batch_arrays(batch_size) - for batch_element, state_index in enumerate(indices): - trajectory_indices = [(state_index + j) % self._replay_capacity - for j in range(self._update_horizon)] - trajectory_terminals = self._store['terminal'][trajectory_indices] - is_terminal_transition = trajectory_terminals.any() - if not is_terminal_transition: - trajectory_length = self._update_horizon - else: - # np.argmax of a bool array returns the index of the first True. - trajectory_length = np.argmax(trajectory_terminals.astype(np.bool), - 0) + 1 - next_state_index = state_index + trajectory_length - trajectory_discount_vector = ( - self._cumulative_discount_vector[:trajectory_length]) - trajectory_rewards = self.get_range(self._store['reward'], state_index, - next_state_index) - - # Fill the contents of each array in the sampled batch. - assert len(transition_elements) == len(batch_arrays) - for element_array, element in zip(batch_arrays, transition_elements): - if element.name == 'state': - element_array[batch_element] = self.get_observation_stack(state_index) - elif element.name == 'reward': - # compute the discounted sum of rewards in the trajectory. - element_array[batch_element] = np.sum( - trajectory_discount_vector * trajectory_rewards, axis=0) - elif element.name == 'next_state': - element_array[batch_element] = self.get_observation_stack( - (next_state_index) % self._replay_capacity) - elif element.name in ('next_action', 'next_reward'): - element_array[batch_element] = ( - self._store[element.name.lstrip('next_')][(next_state_index) % - self._replay_capacity]) - elif element.name == 'terminal': - element_array[batch_element] = is_terminal_transition - elif element.name == 'indices': - element_array[batch_element] = state_index - elif element.name in self._store.keys(): - element_array[batch_element] = ( - self._store[element.name][state_index]) - # We assume the other elements are filled in by the subclass. - - return batch_arrays - - def get_transition_elements(self, batch_size=None): - """Returns a 'type signature' for sample_transition_batch. - - Args: - batch_size: int, number of transitions returned. If None, the default - batch_size will be used. - Returns: - signature: A namedtuple describing the method's return type signature. - """ - batch_size = self._batch_size if batch_size is None else batch_size - - transition_elements = [ - ReplayElement('state', (batch_size,) + self._state_shape, - self._observation_dtype), - ReplayElement('action', (batch_size,) + self._action_shape, - self._action_dtype), - ReplayElement('reward', (batch_size,) + self._reward_shape, - self._reward_dtype), - ReplayElement('next_state', (batch_size,) + self._state_shape, - self._observation_dtype), - ReplayElement('next_action', (batch_size,) + self._action_shape, - self._action_dtype), - ReplayElement('next_reward', (batch_size,) + self._reward_shape, - self._reward_dtype), - ReplayElement('terminal', (batch_size,), np.uint8), - ReplayElement('indices', (batch_size,), np.int32) - ] - for element in self._extra_storage_types: - transition_elements.append( - ReplayElement(element.name, (batch_size,) + tuple(element.shape), - element.type)) - return transition_elements - - def _generate_filename(self, checkpoint_dir, name, suffix): - return os.path.join(checkpoint_dir, '{}_ckpt.{}.gz'.format(name, suffix)) - - def _return_checkpointable_elements(self): - """Return the dict of elements of the class for checkpointing. - - Returns: - checkpointable_elements: dict containing all non private (starting with - _) members + all the arrays inside self._store. - """ - checkpointable_elements = {} - for member_name, member in self.__dict__.items(): - if member_name == '_store': - for array_name, array in self._store.items(): - checkpointable_elements[STORE_FILENAME_PREFIX + array_name] = array - elif not member_name.startswith('_'): - checkpointable_elements[member_name] = member - return checkpointable_elements - - def save(self, checkpoint_dir, iteration_number): - """Save the OutOfGraphReplayBuffer attributes into a file. - - This method will save all the replay buffer's state in a single file. - - Args: - checkpoint_dir: str, the directory where numpy checkpoint files should be - saved. - iteration_number: int, iteration_number to use as a suffix in naming - numpy checkpoint files. - """ - if not tf.gfile.Exists(checkpoint_dir): - return - - checkpointable_elements = self._return_checkpointable_elements() - - for attr in checkpointable_elements: - filename = self._generate_filename(checkpoint_dir, attr, iteration_number) - with tf.gfile.Open(filename, 'wb') as f: - with gzip.GzipFile(fileobj=f) as outfile: - # Checkpoint the np arrays in self._store with np.save instead of - # pickling the dictionary is critical for file size and performance. - # STORE_FILENAME_PREFIX indicates that the variable is contained in - # self._store. - if attr.startswith(STORE_FILENAME_PREFIX): - array_name = attr[len(STORE_FILENAME_PREFIX):] - np.save(outfile, self._store[array_name], allow_pickle=False) - # Some numpy arrays might not be part of storage - elif isinstance(self.__dict__[attr], np.ndarray): - np.save(outfile, self.__dict__[attr], allow_pickle=False) - else: - pickle.dump(self.__dict__[attr], outfile) - - # After writing a checkpoint file, we garbage collect the checkpoint file - # that is four versions old. - stale_iteration_number = iteration_number - CHECKPOINT_DURATION - if stale_iteration_number >= 0: - stale_filename = self._generate_filename(checkpoint_dir, attr, - stale_iteration_number) - try: - tf.gfile.Remove(stale_filename) - except tf.errors.NotFoundError: - pass - - def load(self, checkpoint_dir, suffix): - """Restores the object from bundle_dictionary and numpy checkpoints. - - Args: - checkpoint_dir: str, the directory where to read the numpy checkpointed - files from. - suffix: str, the suffix to use in numpy checkpoint files. - - Raises: - NotFoundError: If not all expected files are found in directory. - """ - save_elements = self._return_checkpointable_elements() - # We will first make sure we have all the necessary files available to avoid - # loading a partially-specified (i.e. corrupted) replay buffer. - for attr in save_elements: - filename = self._generate_filename(checkpoint_dir, attr, suffix) - if not tf.gfile.Exists(filename): - raise tf.errors.NotFoundError(None, None, - 'Missing file: {}'.format(filename)) - # If we've reached this point then we have verified that all expected files - # are available. - for attr in save_elements: - filename = self._generate_filename(checkpoint_dir, attr, suffix) - with tf.gfile.Open(filename, 'rb') as f: - with gzip.GzipFile(fileobj=f) as infile: - if attr.startswith(STORE_FILENAME_PREFIX): - array_name = attr[len(STORE_FILENAME_PREFIX):] - self._store[array_name] = np.load(infile, allow_pickle=False) - elif isinstance(self.__dict__[attr], np.ndarray): - self.__dict__[attr] = np.load(infile, allow_pickle=False) - else: - self.__dict__[attr] = pickle.load(infile) - - -@gin.configurable(blacklist=['observation_shape', 'stack_size', - 'update_horizon', 'gamma']) -class WrappedReplayBuffer(object): - """Wrapper of OutOfGraphReplayBuffer with an in graph sampling mechanism. - - Usage: - To add a transition: call the add function. - - To sample a batch: Construct operations that depend on any of the - tensors is the transition dictionary. Every sess.run - that requires any of these tensors will sample a new - transition. - """ - - def __init__(self, - observation_shape, - stack_size, - use_staging=True, - replay_capacity=1000000, - batch_size=32, - update_horizon=1, - gamma=0.99, - wrapped_memory=None, - max_sample_attempts=MAX_SAMPLE_ATTEMPTS, - extra_storage_types=None, - observation_dtype=np.uint8, - action_shape=(), - action_dtype=np.int32, - reward_shape=(), - reward_dtype=np.float32): - """Initializes WrappedReplayBuffer. - - Args: - observation_shape: tuple of ints. - stack_size: int, number of frames to use in state stack. - use_staging: bool, when True it would use a staging area to prefetch - the next sampling batch. - replay_capacity: int, number of transitions to keep in memory. - batch_size: int. - update_horizon: int, length of update ('n' in n-step update). - gamma: int, the discount factor. - wrapped_memory: The 'inner' memory data structure. If None, - it creates the standard DQN replay memory. - max_sample_attempts: int, the maximum number of attempts allowed to - get a sample. - extra_storage_types: list of ReplayElements defining the type of the extra - contents that will be stored and returned by sample_transition_batch. - observation_dtype: np.dtype, type of the observations. Defaults to - np.uint8 for Atari 2600. - action_shape: tuple of ints, the shape for the action vector. Empty tuple - means the action is a scalar. - action_dtype: np.dtype, type of elements in the action. - reward_shape: tuple of ints, the shape of the reward vector. Empty tuple - means the reward is a scalar. - reward_dtype: np.dtype, type of elements in the reward. - - Raises: - ValueError: If update_horizon is not positive. - ValueError: If discount factor is not in [0, 1]. - """ - if replay_capacity < update_horizon + 1: - raise ValueError( - 'Update horizon ({}) should be significantly smaller ' - 'than replay capacity ({}).'.format(update_horizon, replay_capacity)) - if not update_horizon >= 1: - raise ValueError('Update horizon must be positive.') - if not 0.0 <= gamma <= 1.0: - raise ValueError('Discount factor (gamma) must be in [0, 1].') - - self.batch_size = batch_size - - # Mainly used to allow subclasses to pass self.memory. - if wrapped_memory is not None: - self.memory = wrapped_memory - else: - self.memory = OutOfGraphReplayBuffer( - observation_shape, - stack_size, - replay_capacity, - batch_size, - update_horizon, - gamma, - max_sample_attempts, - observation_dtype=observation_dtype, - extra_storage_types=extra_storage_types, - action_shape=action_shape, - action_dtype=action_dtype, - reward_shape=reward_shape, - reward_dtype=reward_dtype) - - self.create_sampling_ops(use_staging) - - def add(self, observation, action, reward, terminal, *args): - """Adds a transition to the replay memory. - - Since the next_observation in the transition will be the observation added - next there is no need to pass it. - - If the replay memory is at capacity the oldest transition will be discarded. - - Args: - observation: np.array with shape observation_shape. - action: int, the action in the transition. - reward: float, the reward received in the transition. - terminal: A uint8 acting as a boolean indicating whether the transition - was terminal (1) or not (0). - *args: extra contents with shapes and dtypes according to - extra_storage_types. - """ - self.memory.add(observation, action, reward, terminal, *args) - - def create_sampling_ops(self, use_staging): - """Creates the ops necessary to sample from the replay buffer. - - Creates the transition dictionary containing the sampling tensors. - - Args: - use_staging: bool, when True it would use a staging area to prefetch - the next sampling batch. - """ - with tf.name_scope('sample_replay'): - with tf.device('/cpu:*'): - transition_type = self.memory.get_transition_elements() - transition_tensors = tf.py_func( - self.memory.sample_transition_batch, [], - [return_entry.type for return_entry in transition_type], - name='replay_sample_py_func') - self._set_transition_shape(transition_tensors, transition_type) - if use_staging: - transition_tensors = self._set_up_staging(transition_tensors) - self._set_transition_shape(transition_tensors, transition_type) - - # Unpack sample transition into member variables. - self.unpack_transition(transition_tensors, transition_type) - - def _set_transition_shape(self, transition, transition_type): - """Set shape for each element in the transition. - - Args: - transition: tuple of tf.Tensors. - transition_type: tuple of ReplayElements descriving the shapes of the - respective tensors. - """ - for element, element_type in zip(transition, transition_type): - element.set_shape(element_type.shape) - - def _set_up_staging(self, transition): - """Sets up staging ops for prefetching the next transition. - - This allows us to hide the py_func latency. To do so we use a staging area - to pre-fetch the next batch of transitions. - - Args: - transition: tuple of tf.Tensors with shape - memory.get_transition_elements(). - - Returns: - prefetched_transition: tuple of tf.Tensors with shape - memory.get_transition_elements() that have been previously prefetched. - """ - transition_type = self.memory.get_transition_elements() - - # Create the staging area in CPU. - prefetch_area = tf.contrib.staging.StagingArea( - [shape_with_type.type for shape_with_type in transition_type]) - - # Store prefetch op for tests, but keep it private -- users should not be - # calling _prefetch_batch. - self._prefetch_batch = prefetch_area.put(transition) - initial_prefetch = tf.cond( - tf.equal(prefetch_area.size(), 0), - lambda: prefetch_area.put(transition), tf.no_op) - - # Every time a transition is sampled self.prefetch_batch will be - # called. If the staging area is empty, two put ops will be called. - with tf.control_dependencies([self._prefetch_batch, initial_prefetch]): - prefetched_transition = prefetch_area.get() - - return prefetched_transition - - def unpack_transition(self, transition_tensors, transition_type): - """Unpacks the given transition into member variables. - - Args: - transition_tensors: tuple of tf.Tensors. - transition_type: tuple of ReplayElements matching transition_tensors. - """ - self.transition = collections.OrderedDict() - for element, element_type in zip(transition_tensors, transition_type): - self.transition[element_type.name] = element - - # TODO(bellemare): These are legacy and should probably be removed in - # future versions. - self.states = self.transition['state'] - self.actions = self.transition['action'] - self.rewards = self.transition['reward'] - self.next_states = self.transition['next_state'] - self.next_actions = self.transition['next_action'] - self.next_rewards = self.transition['next_reward'] - self.terminals = self.transition['terminal'] - self.indices = self.transition['indices'] - - def save(self, checkpoint_dir, iteration_number): - """Save the underlying replay buffer's contents in a file. - - Args: - checkpoint_dir: str, the directory where to read the numpy checkpointed - files from. - iteration_number: int, the iteration_number to use as a suffix in naming - numpy checkpoint files. - """ - self.memory.save(checkpoint_dir, iteration_number) - - def load(self, checkpoint_dir, suffix): - """Loads the replay buffer's state from a saved file. - - Args: - checkpoint_dir: str, the directory where to read the numpy checkpointed - files from. - suffix: str, the suffix to use in numpy checkpoint files. - """ - self.memory.load(checkpoint_dir, suffix) +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""The standard DQN replay memory. + +This implementation is an out-of-graph replay memory + in-graph wrapper. It +supports vanilla n-step updates of the form typically found in the literature, +i.e. where rewards are accumulated for n steps and the intermediate trajectory +is not exposed to the agent. This does not allow, for example, performing +off-policy corrections. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import gzip +import math +import os +import pickle + +import numpy as np +import tensorflow as tf + +import gin.tf + +# Defines a type describing part of the tuple returned by the replay +# memory. Each element of the tuple is a tensor of shape [batch, ...] where +# ... is defined the 'shape' field of ReplayElement. The tensor type is +# given by the 'type' field. The 'name' field is for convenience and ease of +# debugging. +ReplayElement = ( + collections.namedtuple('shape_type', ['name', 'shape', 'type'])) + +# A prefix that can not collide with variable names for checkpoint files. +STORE_FILENAME_PREFIX = '$store$_' + +# This constant determines how many iterations a checkpoint is kept for. +CHECKPOINT_DURATION = 4 +MAX_SAMPLE_ATTEMPTS = 1000 + + +def invalid_range(cursor, replay_capacity, stack_size, update_horizon): + """Returns a array with the indices of cursor-related invalid transitions. + + There are update_horizon + stack_size invalid indices: + - The update_horizon indices before the cursor, because we do not have a + valid N-step transition (including the next state). + - The stack_size indices on or immediately after the cursor. + If N = update_horizon, K = stack_size, and the cursor is at c, invalid + indices are: + c - N, c - N + 1, ..., c, c + 1, ..., c + K - 1. + + It handles special cases in a circular buffer in the beginning and the end. + + Args: + cursor: int, the position of the cursor. + replay_capacity: int, the size of the replay memory. + stack_size: int, the size of the stacks returned by the replay memory. + update_horizon: int, the agent's update horizon. + Returns: + np.array of size stack_size with the invalid indices. + """ + assert cursor < replay_capacity + return np.array( + [(cursor - update_horizon + i) % replay_capacity + for i in range(stack_size + update_horizon)]) + + +class OutOfGraphReplayBuffer(object): + """A simple out-of-graph Replay Buffer. + + Stores transitions, state, action, reward, next_state, terminal (and any + extra contents specified) in a circular buffer and provides a uniform + transition sampling function. + + When the states consist of stacks of observations storing the states is + inefficient. This class writes observations and constructs the stacked states + at sample time. + + Attributes: + add_count: int, counter of how many transitions have been added (including + the blank ones at the beginning of an episode). + invalid_range: np.array, an array with the indices of cursor-related invalid + transitions + """ + + def __init__(self, + observation_shape, + stack_size, + replay_capacity, + batch_size, + update_horizon=1, + gamma=0.99, + max_sample_attempts=MAX_SAMPLE_ATTEMPTS, + extra_storage_types=None, + observation_dtype=np.uint8, + action_shape=(), + action_dtype=np.int32, + reward_shape=(), + reward_dtype=np.float32): + """Initializes OutOfGraphReplayBuffer. + + Args: + observation_shape: tuple of ints. + stack_size: int, number of frames to use in state stack. + replay_capacity: int, number of transitions to keep in memory. + batch_size: int. + update_horizon: int, length of update ('n' in n-step update). + gamma: int, the discount factor. + max_sample_attempts: int, the maximum number of attempts allowed to + get a sample. + extra_storage_types: list of ReplayElements defining the type of the extra + contents that will be stored and returned by sample_transition_batch. + observation_dtype: np.dtype, type of the observations. Defaults to + np.uint8 for Atari 2600. + action_shape: tuple of ints, the shape for the action vector. Empty tuple + means the action is a scalar. + action_dtype: np.dtype, type of elements in the action. + reward_shape: tuple of ints, the shape of the reward vector. Empty tuple + means the reward is a scalar. + reward_dtype: np.dtype, type of elements in the reward. + + Raises: + ValueError: If replay_capacity is too small to hold at least one + transition. + """ + assert isinstance(observation_shape, tuple) + if replay_capacity < update_horizon + stack_size: + raise ValueError('There is not enough capacity to cover ' + 'update_horizon and stack_size.') + + tf.logging.info( + 'Creating a %s replay memory with the following parameters:', + self.__class__.__name__) + tf.logging.info('\t observation_shape: %s', str(observation_shape)) + tf.logging.info('\t observation_dtype: %s', str(observation_dtype)) + tf.logging.info('\t stack_size: %d', stack_size) + tf.logging.info('\t replay_capacity: %d', replay_capacity) + tf.logging.info('\t batch_size: %d', batch_size) + tf.logging.info('\t update_horizon: %d', update_horizon) + tf.logging.info('\t gamma: %f', gamma) + + self._action_shape = action_shape + self._action_dtype = action_dtype + self._reward_shape = reward_shape + self._reward_dtype = reward_dtype + self._observation_shape = observation_shape + self._stack_size = stack_size + self._state_shape = self._observation_shape + (self._stack_size,) + self._replay_capacity = replay_capacity + self._batch_size = batch_size + self._update_horizon = update_horizon + self._gamma = gamma + self._observation_dtype = observation_dtype + self._max_sample_attempts = max_sample_attempts + if extra_storage_types: + self._extra_storage_types = extra_storage_types + else: + self._extra_storage_types = [] + self._create_storage() + self.add_count = np.array(0) + self.invalid_range = np.zeros((self._stack_size)) + # When the horizon is > 1, we compute the sum of discounted rewards as a dot + # product using the precomputed vector . + self._cumulative_discount_vector = np.array( + [math.pow(self._gamma, n) for n in range(update_horizon)], + dtype=np.float32) + + def _create_storage(self): + """Creates the numpy arrays used to store transitions. + """ + self._store = {} + for storage_element in self.get_storage_signature(): + array_shape = [self._replay_capacity] + list(storage_element.shape) + self._store[storage_element.name] = np.empty( + array_shape, dtype=storage_element.type) + + def get_add_args_signature(self): + """The signature of the add function. + + Note - Derived classes may return a different signature. + + Returns: + list of ReplayElements defining the type of the argument signature needed + by the add function. + """ + return self.get_storage_signature() + + def get_storage_signature(self): + """Returns a default list of elements to be stored in this replay memory. + + Note - Derived classes may return a different signature. + + Returns: + list of ReplayElements defining the type of the contents stored. + """ + storage_elements = [ + ReplayElement('observation', self._observation_shape, + self._observation_dtype), + ReplayElement('action', self._action_shape, self._action_dtype), + ReplayElement('reward', self._reward_shape, self._reward_dtype), + ReplayElement('terminal', (), np.uint8) + ] + + for extra_replay_element in self._extra_storage_types: + storage_elements.append(extra_replay_element) + return storage_elements + + def _add_zero_transition(self): + """Adds a padding transition filled with zeros (Used in episode beginnings). + """ + zero_transition = [] + for element_type in self.get_add_args_signature(): + zero_transition.append( + np.zeros(element_type.shape, dtype=element_type.type)) + self._add(*zero_transition) + + def add(self, observation, action, reward, terminal, *args): + """Adds a transition to the replay memory. + + This function checks the types and handles the padding at the beginning of + an episode. Then it calls the _add function. + + Since the next_observation in the transition will be the observation added + next there is no need to pass it. + + If the replay memory is at capacity the oldest transition will be discarded. + + Args: + observation: np.array with shape observation_shape. + action: int, the action in the transition. + reward: float, the reward received in the transition. + terminal: A uint8 acting as a boolean indicating whether the transition + was terminal (1) or not (0). + *args: extra contents with shapes and dtypes according to + extra_storage_types. + """ + self._check_add_types(observation, action, reward, terminal, *args) + if self.is_empty() or self._store['terminal'][self.cursor() - 1] == 1: + for _ in range(self._stack_size - 1): + # Child classes can rely on the padding transitions being filled with + # zeros. This is useful when there is a priority argument. + self._add_zero_transition() + self._add(observation, action, reward, terminal, *args) + + def _add(self, *args): + """Internal add method to add to the storage arrays. + + Args: + *args: All the elements in a transition. + """ + cursor = self.cursor() + + arg_names = [e.name for e in self.get_add_args_signature()] + for arg_name, arg in zip(arg_names, args): + self._store[arg_name][cursor] = arg + + self.add_count += 1 + self.invalid_range = invalid_range( + self.cursor(), self._replay_capacity, self._stack_size, + self._update_horizon) + + def _check_add_types(self, *args): + """Checks if args passed to the add method match those of the storage. + + Args: + *args: Args whose types need to be validated. + + Raises: + ValueError: If args have wrong shape or dtype. + """ + if len(args) != len(self.get_add_args_signature()): + raise ValueError('Add expects {} elements, received {}'.format( + len(self.get_add_args_signature()), len(args))) + for arg_element, store_element in zip(args, self.get_add_args_signature()): + if isinstance(arg_element, np.ndarray): + arg_shape = arg_element.shape + elif isinstance(arg_element, tuple) or isinstance(arg_element, list): + # TODO(b/80536437). This is not efficient when arg_element is a list. + arg_shape = np.array(arg_element).shape + else: + # Assume it is scalar. + arg_shape = tuple() + store_element_shape = tuple(store_element.shape) + if arg_shape != store_element_shape: + raise ValueError('arg has shape {}, expected {}'.format( + arg_shape, store_element_shape)) + + def is_empty(self): + """Is the Replay Buffer empty?""" + return self.add_count == 0 + + def is_full(self): + """Is the Replay Buffer full?""" + return self.add_count >= self._replay_capacity + + def cursor(self): + """Index to the location where the next transition will be written.""" + return self.add_count % self._replay_capacity + + def get_range(self, array, start_index, end_index): + """Returns the range of array at the index handling wraparound if necessary. + + Args: + array: np.array, the array to get the stack from. + start_index: int, index to the start of the range to be returned. Range + will wraparound if start_index is smaller than 0. + end_index: int, exclusive end index. Range will wraparound if end_index + exceeds replay_capacity. + + Returns: + np.array, with shape [end_index - start_index, array.shape[1:]]. + """ + assert end_index > start_index, 'end_index must be larger than start_index' + assert end_index >= 0 + assert start_index < self._replay_capacity + if not self.is_full(): + assert end_index <= self.cursor(), ( + 'Index {} has not been added.'.format(start_index)) + + # Fast slice read when there is no wraparound. + if start_index % self._replay_capacity < end_index % self._replay_capacity: + return_array = array[start_index:end_index, ...] + # Slow list read. + else: + indices = [(start_index + i) % self._replay_capacity + for i in range(end_index - start_index)] + return_array = array[indices, ...] + return return_array + + def get_observation_stack(self, index): + return self._get_element_stack(index, 'observation') + + def _get_element_stack(self, index, element_name): + state = self.get_range(self._store[element_name], + index - self._stack_size + 1, index + 1) + # The stacking axis is 0 but the agent expects as the last axis. + return np.moveaxis(state, 0, -1) + + def get_terminal_stack(self, index): + return self.get_range(self._store['terminal'], index - self._stack_size + 1, + index + 1) + + def is_valid_transition(self, index): + """Checks if the index contains a valid transition. + + Checks for collisions with the end of episodes and the current position + of the cursor. + + Args: + index: int, the index to the state in the transition. + + Returns: + Is the index valid: Boolean. + + """ + # Check the index is in the valid range + if index < 0 or index >= self._replay_capacity: + return False + if not self.is_full(): + # The indices and next_indices must be smaller than the cursor. + if index >= self.cursor() - self._update_horizon: + return False + # The first few indices contain the padding states of the first episode. + if index < self._stack_size - 1: + return False + + # Skip transitions that straddle the cursor. + if index in set(self.invalid_range): + return False + + # If there are terminal flags in any other frame other than the last one + # the stack is not valid, so don't sample it. + if self.get_terminal_stack(index)[:-1].any(): + return False + + return True + + def _create_batch_arrays(self, batch_size): + """Create a tuple of arrays with the type of get_transition_elements. + + When using the WrappedReplayBuffer with staging enabled it is important to + create new arrays every sample because StaginArea keeps a pointer to the + returned arrays. + + Args: + batch_size: (int) number of transitions returned. If None the default + batch_size will be used. + + Returns: + Tuple of np.arrays with the shape and type of get_transition_elements. + """ + transition_elements = self.get_transition_elements(batch_size) + batch_arrays = [] + for element in transition_elements: + batch_arrays.append(np.empty(element.shape, dtype=element.type)) + return tuple(batch_arrays) + + def sample_index_batch(self, batch_size): + """Returns a batch of valid indices sampled uniformly. + + Args: + batch_size: int, number of indices returned. + + Returns: + list of ints, a batch of valid indices sampled uniformly. + + Raises: + RuntimeError: If the batch was not constructed after maximum number of + tries. + """ + if self.is_full(): + # add_count >= self._replay_capacity > self._stack_size + min_id = self.cursor() - self._replay_capacity + self._stack_size - 1 + max_id = self.cursor() - self._update_horizon + else: + # add_count < self._replay_capacity + min_id = self._stack_size - 1 + max_id = self.cursor() - self._update_horizon + if max_id <= min_id: + raise RuntimeError('Cannot sample a batch with fewer than stack size ' + '({}) + update_horizon ({}) transitions.'. + format(self._stack_size, self._update_horizon)) + + indices = [] + attempt_count = 0 + while (len(indices) < batch_size and + attempt_count < self._max_sample_attempts): + attempt_count += 1 + index = np.random.randint(min_id, max_id) % self._replay_capacity + if self.is_valid_transition(index): + indices.append(index) + if len(indices) != batch_size: + raise RuntimeError( + 'Max sample attempts: Tried {} times but only sampled {}' + ' valid indices. Batch size is {}'. + format(self._max_sample_attempts, len(indices), batch_size)) + + return indices + + def sample_transition_batch(self, batch_size=None, indices=None): + """Returns a batch of transitions (including any extra contents). + + If get_transition_elements has been overridden and defines elements not + stored in self._store, an empty array will be returned and it will be + left to the child class to fill it. For example, for the child class + OutOfGraphPrioritizedReplayBuffer, the contents of the + sampling_probabilities are stored separately in a sum tree. + + When the transition is terminal next_state_batch has undefined contents. + + NOTE: This transition contains the indices of the sampled elements. These + are only valid during the call to sample_transition_batch, i.e. they may + be used by subclasses of this replay buffer but may point to different data + as soon as sampling is done. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + indices: None or list of ints, the indices of every transition in the + batch. If None, sample the indices uniformly. + + Returns: + transition_batch: tuple of np.arrays with the shape and type as in + get_transition_elements(). + + Raises: + ValueError: If an element to be sampled is missing from the replay buffer. + """ + if batch_size is None: + batch_size = self._batch_size + if indices is None: + indices = self.sample_index_batch(batch_size) + assert len(indices) == batch_size + + transition_elements = self.get_transition_elements(batch_size) + batch_arrays = self._create_batch_arrays(batch_size) + for batch_element, state_index in enumerate(indices): + trajectory_indices = [(state_index + j) % self._replay_capacity + for j in range(self._update_horizon)] + trajectory_terminals = self._store['terminal'][trajectory_indices] + is_terminal_transition = trajectory_terminals.any() + if not is_terminal_transition: + trajectory_length = self._update_horizon + else: + # np.argmax of a bool array returns the index of the first True. + trajectory_length = np.argmax(trajectory_terminals.astype(np.bool), + 0) + 1 + next_state_index = state_index + trajectory_length + trajectory_discount_vector = ( + self._cumulative_discount_vector[:trajectory_length]) + trajectory_rewards = self.get_range(self._store['reward'], state_index, + next_state_index) + + # Fill the contents of each array in the sampled batch. + assert len(transition_elements) == len(batch_arrays) + for element_array, element in zip(batch_arrays, transition_elements): + if element.name == 'state': + element_array[batch_element] = self.get_observation_stack(state_index) + elif element.name == 'reward': + # compute the discounted sum of rewards in the trajectory. + element_array[batch_element] = np.sum( + trajectory_discount_vector * trajectory_rewards, axis=0) + elif element.name == 'next_state': + element_array[batch_element] = self.get_observation_stack( + (next_state_index) % self._replay_capacity) + elif element.name in ('next_action', 'next_reward'): + element_array[batch_element] = ( + self._store[element.name.lstrip('next_')][(next_state_index) % + self._replay_capacity]) + elif element.name == 'terminal': + element_array[batch_element] = is_terminal_transition + elif element.name == 'indices': + element_array[batch_element] = state_index + elif element.name in self._store.keys(): + element_array[batch_element] = ( + self._store[element.name][state_index]) + # We assume the other elements are filled in by the subclass. + + return batch_arrays + + def get_transition_elements(self, batch_size=None): + """Returns a 'type signature' for sample_transition_batch. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + Returns: + signature: A namedtuple describing the method's return type signature. + """ + batch_size = self._batch_size if batch_size is None else batch_size + + transition_elements = [ + ReplayElement('state', (batch_size,) + self._state_shape, + self._observation_dtype), + ReplayElement('action', (batch_size,) + self._action_shape, + self._action_dtype), + ReplayElement('reward', (batch_size,) + self._reward_shape, + self._reward_dtype), + ReplayElement('next_state', (batch_size,) + self._state_shape, + self._observation_dtype), + ReplayElement('next_action', (batch_size,) + self._action_shape, + self._action_dtype), + ReplayElement('next_reward', (batch_size,) + self._reward_shape, + self._reward_dtype), + ReplayElement('terminal', (batch_size,), np.uint8), + ReplayElement('indices', (batch_size,), np.int32) + ] + for element in self._extra_storage_types: + transition_elements.append( + ReplayElement(element.name, (batch_size,) + tuple(element.shape), + element.type)) + return transition_elements + + def _generate_filename(self, checkpoint_dir, name, suffix): + return os.path.join(checkpoint_dir, '{}_ckpt.{}.gz'.format(name, suffix)) + + def _return_checkpointable_elements(self): + """Return the dict of elements of the class for checkpointing. + + Returns: + checkpointable_elements: dict containing all non private (starting with + _) members + all the arrays inside self._store. + """ + checkpointable_elements = {} + for member_name, member in self.__dict__.items(): + if member_name == '_store': + for array_name, array in self._store.items(): + checkpointable_elements[STORE_FILENAME_PREFIX + array_name] = array + elif not member_name.startswith('_'): + checkpointable_elements[member_name] = member + return checkpointable_elements + + def save(self, checkpoint_dir, iteration_number): + """Save the OutOfGraphReplayBuffer attributes into a file. + + This method will save all the replay buffer's state in a single file. + + Args: + checkpoint_dir: str, the directory where numpy checkpoint files should be + saved. + iteration_number: int, iteration_number to use as a suffix in naming + numpy checkpoint files. + """ + if not tf.gfile.Exists(checkpoint_dir): + return + + checkpointable_elements = self._return_checkpointable_elements() + + for attr in checkpointable_elements: + filename = self._generate_filename(checkpoint_dir, attr, iteration_number) + with tf.gfile.Open(filename, 'wb') as f: + with gzip.GzipFile(fileobj=f) as outfile: + # Checkpoint the np arrays in self._store with np.save instead of + # pickling the dictionary is critical for file size and performance. + # STORE_FILENAME_PREFIX indicates that the variable is contained in + # self._store. + if attr.startswith(STORE_FILENAME_PREFIX): + array_name = attr[len(STORE_FILENAME_PREFIX):] + np.save(outfile, self._store[array_name], allow_pickle=False) + # Some numpy arrays might not be part of storage + elif isinstance(self.__dict__[attr], np.ndarray): + np.save(outfile, self.__dict__[attr], allow_pickle=False) + else: + pickle.dump(self.__dict__[attr], outfile) + + # After writing a checkpoint file, we garbage collect the checkpoint file + # that is four versions old. + stale_iteration_number = iteration_number - CHECKPOINT_DURATION + if stale_iteration_number >= 0: + stale_filename = self._generate_filename(checkpoint_dir, attr, + stale_iteration_number) + try: + tf.gfile.Remove(stale_filename) + except tf.errors.NotFoundError: + pass + + def load(self, checkpoint_dir, suffix): + """Restores the object from bundle_dictionary and numpy checkpoints. + + Args: + checkpoint_dir: str, the directory where to read the numpy checkpointed + files from. + suffix: str, the suffix to use in numpy checkpoint files. + + Raises: + NotFoundError: If not all expected files are found in directory. + """ + save_elements = self._return_checkpointable_elements() + # We will first make sure we have all the necessary files available to avoid + # loading a partially-specified (i.e. corrupted) replay buffer. + for attr in save_elements: + filename = self._generate_filename(checkpoint_dir, attr, suffix) + if not tf.gfile.Exists(filename): + raise tf.errors.NotFoundError(None, None, + 'Missing file: {}'.format(filename)) + # If we've reached this point then we have verified that all expected files + # are available. + for attr in save_elements: + filename = self._generate_filename(checkpoint_dir, attr, suffix) + with tf.gfile.Open(filename, 'rb') as f: + with gzip.GzipFile(fileobj=f) as infile: + if attr.startswith(STORE_FILENAME_PREFIX): + array_name = attr[len(STORE_FILENAME_PREFIX):] + self._store[array_name] = np.load(infile, allow_pickle=False) + elif isinstance(self.__dict__[attr], np.ndarray): + self.__dict__[attr] = np.load(infile, allow_pickle=False) + else: + self.__dict__[attr] = pickle.load(infile) + + +@gin.configurable(blacklist=['observation_shape', 'stack_size', + 'update_horizon', 'gamma']) +class WrappedReplayBuffer(object): + """Wrapper of OutOfGraphReplayBuffer with an in graph sampling mechanism. + + Usage: + To add a transition: call the add function. + + To sample a batch: Construct operations that depend on any of the + tensors is the transition dictionary. Every sess.run + that requires any of these tensors will sample a new + transition. + """ + + def __init__(self, + observation_shape, + stack_size, + use_staging=True, + replay_capacity=1000000, + batch_size=32, + update_horizon=1, + gamma=0.99, + wrapped_memory=None, + max_sample_attempts=MAX_SAMPLE_ATTEMPTS, + extra_storage_types=None, + observation_dtype=np.uint8, + action_shape=(), + action_dtype=np.int32, + reward_shape=(), + reward_dtype=np.float32): + """Initializes WrappedReplayBuffer. + + Args: + observation_shape: tuple of ints. + stack_size: int, number of frames to use in state stack. + use_staging: bool, when True it would use a staging area to prefetch + the next sampling batch. + replay_capacity: int, number of transitions to keep in memory. + batch_size: int. + update_horizon: int, length of update ('n' in n-step update). + gamma: int, the discount factor. + wrapped_memory: The 'inner' memory data structure. If None, + it creates the standard DQN replay memory. + max_sample_attempts: int, the maximum number of attempts allowed to + get a sample. + extra_storage_types: list of ReplayElements defining the type of the extra + contents that will be stored and returned by sample_transition_batch. + observation_dtype: np.dtype, type of the observations. Defaults to + np.uint8 for Atari 2600. + action_shape: tuple of ints, the shape for the action vector. Empty tuple + means the action is a scalar. + action_dtype: np.dtype, type of elements in the action. + reward_shape: tuple of ints, the shape of the reward vector. Empty tuple + means the reward is a scalar. + reward_dtype: np.dtype, type of elements in the reward. + + Raises: + ValueError: If update_horizon is not positive. + ValueError: If discount factor is not in [0, 1]. + """ + if replay_capacity < update_horizon + 1: + raise ValueError( + 'Update horizon ({}) should be significantly smaller ' + 'than replay capacity ({}).'.format(update_horizon, replay_capacity)) + if not update_horizon >= 1: + raise ValueError('Update horizon must be positive.') + if not 0.0 <= gamma <= 1.0: + raise ValueError('Discount factor (gamma) must be in [0, 1].') + + self.batch_size = batch_size + + # Mainly used to allow subclasses to pass self.memory. + if wrapped_memory is not None: + self.memory = wrapped_memory + else: + self.memory = OutOfGraphReplayBuffer( + observation_shape, + stack_size, + replay_capacity, + batch_size, + update_horizon, + gamma, + max_sample_attempts, + observation_dtype=observation_dtype, + extra_storage_types=extra_storage_types, + action_shape=action_shape, + action_dtype=action_dtype, + reward_shape=reward_shape, + reward_dtype=reward_dtype) + + self.create_sampling_ops(use_staging) + + def add(self, observation, action, reward, terminal, *args): + """Adds a transition to the replay memory. + + Since the next_observation in the transition will be the observation added + next there is no need to pass it. + + If the replay memory is at capacity the oldest transition will be discarded. + + Args: + observation: np.array with shape observation_shape. + action: int, the action in the transition. + reward: float, the reward received in the transition. + terminal: A uint8 acting as a boolean indicating whether the transition + was terminal (1) or not (0). + *args: extra contents with shapes and dtypes according to + extra_storage_types. + """ + self.memory.add(observation, action, reward, terminal, *args) + + def create_sampling_ops(self, use_staging): + """Creates the ops necessary to sample from the replay buffer. + + Creates the transition dictionary containing the sampling tensors. + + Args: + use_staging: bool, when True it would use a staging area to prefetch + the next sampling batch. + """ + with tf.name_scope('sample_replay'): + with tf.device('/cpu:*'): + transition_type = self.memory.get_transition_elements() + transition_tensors = tf.py_func( + self.memory.sample_transition_batch, [], + [return_entry.type for return_entry in transition_type], + name='replay_sample_py_func') + self._set_transition_shape(transition_tensors, transition_type) + if use_staging: + transition_tensors = self._set_up_staging(transition_tensors) + self._set_transition_shape(transition_tensors, transition_type) + + # Unpack sample transition into member variables. + self.unpack_transition(transition_tensors, transition_type) + + def _set_transition_shape(self, transition, transition_type): + """Set shape for each element in the transition. + + Args: + transition: tuple of tf.Tensors. + transition_type: tuple of ReplayElements descriving the shapes of the + respective tensors. + """ + for element, element_type in zip(transition, transition_type): + element.set_shape(element_type.shape) + + def _set_up_staging(self, transition): + """Sets up staging ops for prefetching the next transition. + + This allows us to hide the py_func latency. To do so we use a staging area + to pre-fetch the next batch of transitions. + + Args: + transition: tuple of tf.Tensors with shape + memory.get_transition_elements(). + + Returns: + prefetched_transition: tuple of tf.Tensors with shape + memory.get_transition_elements() that have been previously prefetched. + """ + transition_type = self.memory.get_transition_elements() + + # Create the staging area in CPU. + prefetch_area = tf.contrib.staging.StagingArea( + [shape_with_type.type for shape_with_type in transition_type]) + + # Store prefetch op for tests, but keep it private -- users should not be + # calling _prefetch_batch. + self._prefetch_batch = prefetch_area.put(transition) + initial_prefetch = tf.cond( + tf.equal(prefetch_area.size(), 0), + lambda: prefetch_area.put(transition), tf.no_op) + + # Every time a transition is sampled self.prefetch_batch will be + # called. If the staging area is empty, two put ops will be called. + with tf.control_dependencies([self._prefetch_batch, initial_prefetch]): + prefetched_transition = prefetch_area.get() + + return prefetched_transition + + def unpack_transition(self, transition_tensors, transition_type): + """Unpacks the given transition into member variables. + + Args: + transition_tensors: tuple of tf.Tensors. + transition_type: tuple of ReplayElements matching transition_tensors. + """ + self.transition = collections.OrderedDict() + for element, element_type in zip(transition_tensors, transition_type): + self.transition[element_type.name] = element + + # TODO(bellemare): These are legacy and should probably be removed in + # future versions. + self.states = self.transition['state'] + self.actions = self.transition['action'] + self.rewards = self.transition['reward'] + self.next_states = self.transition['next_state'] + self.next_actions = self.transition['next_action'] + self.next_rewards = self.transition['next_reward'] + self.terminals = self.transition['terminal'] + self.indices = self.transition['indices'] + + def save(self, checkpoint_dir, iteration_number): + """Save the underlying replay buffer's contents in a file. + + Args: + checkpoint_dir: str, the directory where to read the numpy checkpointed + files from. + iteration_number: int, the iteration_number to use as a suffix in naming + numpy checkpoint files. + """ + self.memory.save(checkpoint_dir, iteration_number) + + def load(self, checkpoint_dir, suffix): + """Loads the replay buffer's state from a saved file. + + Args: + checkpoint_dir: str, the directory where to read the numpy checkpointed + files from. + suffix: str, the suffix to use in numpy checkpoint files. + """ + self.memory.load(checkpoint_dir, suffix) diff --git a/dopamine/replay_memory/prioritized_replay_buffer.py b/dopamine/replay_memory/prioritized_replay_buffer.py index 449e76b..07210c6 100644 --- a/dopamine/replay_memory/prioritized_replay_buffer.py +++ b/dopamine/replay_memory/prioritized_replay_buffer.py @@ -1,357 +1,357 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""An implementation of Prioritized Experience Replay (PER). - -This implementation is based on the paper "Prioritized Experience Replay" -by Tom Schaul et al. (2015). Many thanks to Tom Schaul, John Quan, and Matteo -Hessel for providing useful pointers on the algorithm and its implementation. -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - - -from dopamine.replay_memory import circular_replay_buffer -from dopamine.replay_memory import sum_tree -from dopamine.replay_memory.circular_replay_buffer import ReplayElement -import numpy as np -import tensorflow as tf - -import gin.tf - - -class OutOfGraphPrioritizedReplayBuffer( - circular_replay_buffer.OutOfGraphReplayBuffer): - """An out-of-graph Replay Buffer for Prioritized Experience Replay. - - See circular_replay_buffer.py for details. - """ - - def __init__(self, - observation_shape, - stack_size, - replay_capacity, - batch_size, - update_horizon=1, - gamma=0.99, - max_sample_attempts=circular_replay_buffer.MAX_SAMPLE_ATTEMPTS, - extra_storage_types=None, - observation_dtype=np.uint8, - action_shape=(), - action_dtype=np.int32, - reward_shape=(), - reward_dtype=np.float32): - """Initializes OutOfGraphPrioritizedReplayBuffer. - - Args: - observation_shape: tuple of ints. - stack_size: int, number of frames to use in state stack. - replay_capacity: int, number of transitions to keep in memory. - batch_size: int. - update_horizon: int, length of update ('n' in n-step update). - gamma: int, the discount factor. - max_sample_attempts: int, the maximum number of attempts allowed to - get a sample. - extra_storage_types: list of ReplayElements defining the type of the extra - contents that will be stored and returned by sample_transition_batch. - observation_dtype: np.dtype, type of the observations. Defaults to - np.uint8 for Atari 2600. - action_shape: tuple of ints, the shape for the action vector. Empty tuple - means the action is a scalar. - action_dtype: np.dtype, type of elements in the action. - reward_shape: tuple of ints, the shape of the reward vector. Empty tuple - means the reward is a scalar. - reward_dtype: np.dtype, type of elements in the reward. - """ - super(OutOfGraphPrioritizedReplayBuffer, self).__init__( - observation_shape=observation_shape, - stack_size=stack_size, - replay_capacity=replay_capacity, - batch_size=batch_size, - update_horizon=update_horizon, - gamma=gamma, - max_sample_attempts=max_sample_attempts, - extra_storage_types=extra_storage_types, - observation_dtype=observation_dtype, - action_shape=action_shape, - action_dtype=action_dtype, - reward_shape=reward_shape, - reward_dtype=reward_dtype) - - self.sum_tree = sum_tree.SumTree(replay_capacity) - - def get_add_args_signature(self): - """The signature of the add function. - - The signature is the same as the one for OutOfGraphReplayBuffer, with an - added priority. - - Returns: - list of ReplayElements defining the type of the argument signature needed - by the add function. - """ - parent_add_signature = super(OutOfGraphPrioritizedReplayBuffer, - self).get_add_args_signature() - add_signature = parent_add_signature + [ - ReplayElement('priority', (), np.float32) - ] - return add_signature - - def _add(self, *args): - """Internal add method to add to the underlying memory arrays. - - The arguments need to match add_arg_signature. - - If priority is none, it is set to the maximum priority ever seen. - - Args: - *args: All the elements in a transition. - """ - # Use Schaul et al.'s (2015) scheme of setting the priority of new elements - # to the maximum priority so far. - parent_add_args = [] - # Picks out 'priority' from arguments and passes the other arguments to the - # parent method. - for i, element in enumerate(self.get_add_args_signature()): - if element.name == 'priority': - priority = args[i] - else: - parent_add_args.append(args[i]) - - self.sum_tree.set(self.cursor(), priority) - - super(OutOfGraphPrioritizedReplayBuffer, self)._add(*parent_add_args) - - def sample_index_batch(self, batch_size): - """Returns a batch of valid indices sampled as in Schaul et al. (2015). - - Args: - batch_size: int, number of indices returned. - - Returns: - list of ints, a batch of valid indices sampled uniformly. - - Raises: - Exception: If the batch was not constructed after maximum number of tries. - """ - # Sample stratified indices. Some of them might be invalid. - indices = self.sum_tree.stratified_sample(batch_size) - allowed_attempts = self._max_sample_attempts - for i in range(len(indices)): - if not self.is_valid_transition(indices[i]): - if allowed_attempts == 0: - raise RuntimeError( - 'Max sample attempts: Tried {} times but only sampled {}' - ' valid indices. Batch size is {}'. - format(self._max_sample_attempts, i, batch_size)) - index = indices[i] - while not self.is_valid_transition(index) and allowed_attempts > 0: - # If index i is not valid keep sampling others. Note that this - # is not stratified. - index = self.sum_tree.sample() - allowed_attempts -= 1 - indices[i] = index - return indices - - def sample_transition_batch(self, batch_size=None, indices=None): - """Returns a batch of transitions with extra storage and the priorities. - - The extra storage are defined through the extra_storage_types constructor - argument. - - When the transition is terminal next_state_batch has undefined contents. - - Args: - batch_size: int, number of transitions returned. If None, the default - batch_size will be used. - indices: None or list of ints, the indices of every transition in the - batch. If None, sample the indices uniformly. - - Returns: - transition_batch: tuple of np.arrays with the shape and type as in - get_transition_elements(). - """ - transition = (super(OutOfGraphPrioritizedReplayBuffer, self). - sample_transition_batch(batch_size, indices)) - transition_elements = self.get_transition_elements(batch_size) - transition_names = [e.name for e in transition_elements] - probabilities_index = transition_names.index('sampling_probabilities') - indices_index = transition_names.index('indices') - indices = transition[indices_index] - # The parent returned an empty array for the probabilities. Fill it with the - # contents of the sum tree. - transition[probabilities_index][:] = self.get_priority(indices) - return transition - - def set_priority(self, indices, priorities): - """Sets the priority of the given elements according to Schaul et al. - - Args: - indices: np.array with dtype int32, of indices in range - [0, replay_capacity). - priorities: float, the corresponding priorities. - """ - assert indices.dtype == np.int32, ('Indices must be integers, ' - 'given: {}'.format(indices.dtype)) - for index, priority in zip(indices, priorities): - self.sum_tree.set(index, priority) - - def get_priority(self, indices): - """Fetches the priorities correspond to a batch of memory indices. - - For any memory location not yet used, the corresponding priority is 0. - - Args: - indices: np.array with dtype int32, of indices in range - [0, replay_capacity). - - Returns: - priorities: float, the corresponding priorities. - """ - assert indices.shape, 'Indices must be an array.' - assert indices.dtype == np.int32, ('Indices must be int32s, ' - 'given: {}'.format(indices.dtype)) - batch_size = len(indices) - priority_batch = np.empty((batch_size), dtype=np.float32) - for i, memory_index in enumerate(indices): - priority_batch[i] = self.sum_tree.get(memory_index) - return priority_batch - - def get_transition_elements(self, batch_size=None): - """Returns a 'type signature' for sample_transition_batch. - - Args: - batch_size: int, number of transitions returned. If None, the default - batch_size will be used. - Returns: - signature: A namedtuple describing the method's return type signature. - """ - parent_transition_type = ( - super(OutOfGraphPrioritizedReplayBuffer, - self).get_transition_elements(batch_size)) - probablilities_type = [ - ReplayElement('sampling_probabilities', (batch_size,), np.float32) - ] - return parent_transition_type + probablilities_type - - -@gin.configurable(blacklist=['observation_shape', 'stack_size', - 'update_horizon', 'gamma']) -class WrappedPrioritizedReplayBuffer( - circular_replay_buffer.WrappedReplayBuffer): - """Wrapper of OutOfGraphPrioritizedReplayBuffer with in-graph sampling. - - Usage: - - * To add a transition: Call the add function. - - * To sample a batch: Query any of the tensors in the transition dictionary. - Every sess.run that requires any of these tensors will - sample a new transition. - """ - - def __init__(self, - observation_shape, - stack_size, - use_staging=True, - replay_capacity=1000000, - batch_size=32, - update_horizon=1, - gamma=0.99, - max_sample_attempts=circular_replay_buffer.MAX_SAMPLE_ATTEMPTS, - extra_storage_types=None, - observation_dtype=np.uint8, - action_shape=(), - action_dtype=np.int32, - reward_shape=(), - reward_dtype=np.float32): - """Initializes WrappedPrioritizedReplayBuffer. - - Args: - observation_shape: tuple of ints. - stack_size: int, number of frames to use in state stack. - use_staging: bool, when True it would use a staging area to prefetch - the next sampling batch. - replay_capacity: int, number of transitions to keep in memory. - batch_size: int. - update_horizon: int, length of update ('n' in n-step update). - gamma: int, the discount factor. - max_sample_attempts: int, the maximum number of attempts allowed to - get a sample. - extra_storage_types: list of ReplayElements defining the type of the extra - contents that will be stored and returned by sample_transition_batch. - observation_dtype: np.dtype, type of the observations. Defaults to - np.uint8 for Atari 2600. - action_shape: tuple of ints, the shape for the action vector. Empty tuple - means the action is a scalar. - action_dtype: np.dtype, type of elements in the action. - reward_shape: tuple of ints, the shape of the reward vector. Empty tuple - means the reward is a scalar. - reward_dtype: np.dtype, type of elements in the reward. - - Raises: - ValueError: If update_horizon is not positive. - ValueError: If discount factor is not in [0, 1]. - """ - memory = OutOfGraphPrioritizedReplayBuffer( - observation_shape, stack_size, replay_capacity, batch_size, - update_horizon, gamma, max_sample_attempts, - extra_storage_types=extra_storage_types, - observation_dtype=observation_dtype) - super(WrappedPrioritizedReplayBuffer, self).__init__( - observation_shape, - stack_size, - use_staging, - replay_capacity, - batch_size, - update_horizon, - gamma, - wrapped_memory=memory, - extra_storage_types=extra_storage_types, - observation_dtype=observation_dtype, - action_shape=action_shape, - action_dtype=action_dtype, - reward_shape=reward_shape, - reward_dtype=reward_dtype) - - def tf_set_priority(self, indices, priorities): - """Sets the priorities for the given indices. - - Args: - indices: tf.Tensor with dtype int32 and shape [n]. - priorities: tf.Tensor with dtype float and shape [n]. - - Returns: - A tf op setting the priorities for prioritized sampling. - """ - return tf.py_func( - self.memory.set_priority, [indices, priorities], [], - name='prioritized_replay_set_priority_py_func') - - def tf_get_priority(self, indices): - """Gets the priorities for the given indices. - - Args: - indices: tf.Tensor with dtype int32 and shape [n]. - - Returns: - priorities: tf.Tensor with dtype float and shape [n], the priorities at - the indices. - """ - return tf.py_func( - self.memory.get_priority, [indices], - tf.float32, - name='prioritized_replay_get_priority_py_func') +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""An implementation of Prioritized Experience Replay (PER). + +This implementation is based on the paper "Prioritized Experience Replay" +by Tom Schaul et al. (2015). Many thanks to Tom Schaul, John Quan, and Matteo +Hessel for providing useful pointers on the algorithm and its implementation. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + + +from dopamine.replay_memory import circular_replay_buffer +from dopamine.replay_memory import sum_tree +from dopamine.replay_memory.circular_replay_buffer import ReplayElement +import numpy as np +import tensorflow as tf + +import gin.tf + + +class OutOfGraphPrioritizedReplayBuffer( + circular_replay_buffer.OutOfGraphReplayBuffer): + """An out-of-graph Replay Buffer for Prioritized Experience Replay. + + See circular_replay_buffer.py for details. + """ + + def __init__(self, + observation_shape, + stack_size, + replay_capacity, + batch_size, + update_horizon=1, + gamma=0.99, + max_sample_attempts=circular_replay_buffer.MAX_SAMPLE_ATTEMPTS, + extra_storage_types=None, + observation_dtype=np.uint8, + action_shape=(), + action_dtype=np.int32, + reward_shape=(), + reward_dtype=np.float32): + """Initializes OutOfGraphPrioritizedReplayBuffer. + + Args: + observation_shape: tuple of ints. + stack_size: int, number of frames to use in state stack. + replay_capacity: int, number of transitions to keep in memory. + batch_size: int. + update_horizon: int, length of update ('n' in n-step update). + gamma: int, the discount factor. + max_sample_attempts: int, the maximum number of attempts allowed to + get a sample. + extra_storage_types: list of ReplayElements defining the type of the extra + contents that will be stored and returned by sample_transition_batch. + observation_dtype: np.dtype, type of the observations. Defaults to + np.uint8 for Atari 2600. + action_shape: tuple of ints, the shape for the action vector. Empty tuple + means the action is a scalar. + action_dtype: np.dtype, type of elements in the action. + reward_shape: tuple of ints, the shape of the reward vector. Empty tuple + means the reward is a scalar. + reward_dtype: np.dtype, type of elements in the reward. + """ + super(OutOfGraphPrioritizedReplayBuffer, self).__init__( + observation_shape=observation_shape, + stack_size=stack_size, + replay_capacity=replay_capacity, + batch_size=batch_size, + update_horizon=update_horizon, + gamma=gamma, + max_sample_attempts=max_sample_attempts, + extra_storage_types=extra_storage_types, + observation_dtype=observation_dtype, + action_shape=action_shape, + action_dtype=action_dtype, + reward_shape=reward_shape, + reward_dtype=reward_dtype) + + self.sum_tree = sum_tree.SumTree(replay_capacity) + + def get_add_args_signature(self): + """The signature of the add function. + + The signature is the same as the one for OutOfGraphReplayBuffer, with an + added priority. + + Returns: + list of ReplayElements defining the type of the argument signature needed + by the add function. + """ + parent_add_signature = super(OutOfGraphPrioritizedReplayBuffer, + self).get_add_args_signature() + add_signature = parent_add_signature + [ + ReplayElement('priority', (), np.float32) + ] + return add_signature + + def _add(self, *args): + """Internal add method to add to the underlying memory arrays. + + The arguments need to match add_arg_signature. + + If priority is none, it is set to the maximum priority ever seen. + + Args: + *args: All the elements in a transition. + """ + # Use Schaul et al.'s (2015) scheme of setting the priority of new elements + # to the maximum priority so far. + parent_add_args = [] + # Picks out 'priority' from arguments and passes the other arguments to the + # parent method. + for i, element in enumerate(self.get_add_args_signature()): + if element.name == 'priority': + priority = args[i] + else: + parent_add_args.append(args[i]) + + self.sum_tree.set(self.cursor(), priority) + + super(OutOfGraphPrioritizedReplayBuffer, self)._add(*parent_add_args) + + def sample_index_batch(self, batch_size): + """Returns a batch of valid indices sampled as in Schaul et al. (2015). + + Args: + batch_size: int, number of indices returned. + + Returns: + list of ints, a batch of valid indices sampled uniformly. + + Raises: + Exception: If the batch was not constructed after maximum number of tries. + """ + # Sample stratified indices. Some of them might be invalid. + indices = self.sum_tree.stratified_sample(batch_size) + allowed_attempts = self._max_sample_attempts + for i in range(len(indices)): + if not self.is_valid_transition(indices[i]): + if allowed_attempts == 0: + raise RuntimeError( + 'Max sample attempts: Tried {} times but only sampled {}' + ' valid indices. Batch size is {}'. + format(self._max_sample_attempts, i, batch_size)) + index = indices[i] + while not self.is_valid_transition(index) and allowed_attempts > 0: + # If index i is not valid keep sampling others. Note that this + # is not stratified. + index = self.sum_tree.sample() + allowed_attempts -= 1 + indices[i] = index + return indices + + def sample_transition_batch(self, batch_size=None, indices=None): + """Returns a batch of transitions with extra storage and the priorities. + + The extra storage are defined through the extra_storage_types constructor + argument. + + When the transition is terminal next_state_batch has undefined contents. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + indices: None or list of ints, the indices of every transition in the + batch. If None, sample the indices uniformly. + + Returns: + transition_batch: tuple of np.arrays with the shape and type as in + get_transition_elements(). + """ + transition = (super(OutOfGraphPrioritizedReplayBuffer, self). + sample_transition_batch(batch_size, indices)) + transition_elements = self.get_transition_elements(batch_size) + transition_names = [e.name for e in transition_elements] + probabilities_index = transition_names.index('sampling_probabilities') + indices_index = transition_names.index('indices') + indices = transition[indices_index] + # The parent returned an empty array for the probabilities. Fill it with the + # contents of the sum tree. + transition[probabilities_index][:] = self.get_priority(indices) + return transition + + def set_priority(self, indices, priorities): + """Sets the priority of the given elements according to Schaul et al. + + Args: + indices: np.array with dtype int32, of indices in range + [0, replay_capacity). + priorities: float, the corresponding priorities. + """ + assert indices.dtype == np.int32, ('Indices must be integers, ' + 'given: {}'.format(indices.dtype)) + for index, priority in zip(indices, priorities): + self.sum_tree.set(index, priority) + + def get_priority(self, indices): + """Fetches the priorities correspond to a batch of memory indices. + + For any memory location not yet used, the corresponding priority is 0. + + Args: + indices: np.array with dtype int32, of indices in range + [0, replay_capacity). + + Returns: + priorities: float, the corresponding priorities. + """ + assert indices.shape, 'Indices must be an array.' + assert indices.dtype == np.int32, ('Indices must be int32s, ' + 'given: {}'.format(indices.dtype)) + batch_size = len(indices) + priority_batch = np.empty((batch_size), dtype=np.float32) + for i, memory_index in enumerate(indices): + priority_batch[i] = self.sum_tree.get(memory_index) + return priority_batch + + def get_transition_elements(self, batch_size=None): + """Returns a 'type signature' for sample_transition_batch. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + Returns: + signature: A namedtuple describing the method's return type signature. + """ + parent_transition_type = ( + super(OutOfGraphPrioritizedReplayBuffer, + self).get_transition_elements(batch_size)) + probablilities_type = [ + ReplayElement('sampling_probabilities', (batch_size,), np.float32) + ] + return parent_transition_type + probablilities_type + + +@gin.configurable(blacklist=['observation_shape', 'stack_size', + 'update_horizon', 'gamma']) +class WrappedPrioritizedReplayBuffer( + circular_replay_buffer.WrappedReplayBuffer): + """Wrapper of OutOfGraphPrioritizedReplayBuffer with in-graph sampling. + + Usage: + + * To add a transition: Call the add function. + + * To sample a batch: Query any of the tensors in the transition dictionary. + Every sess.run that requires any of these tensors will + sample a new transition. + """ + + def __init__(self, + observation_shape, + stack_size, + use_staging=True, + replay_capacity=1000000, + batch_size=32, + update_horizon=1, + gamma=0.99, + max_sample_attempts=circular_replay_buffer.MAX_SAMPLE_ATTEMPTS, + extra_storage_types=None, + observation_dtype=np.uint8, + action_shape=(), + action_dtype=np.int32, + reward_shape=(), + reward_dtype=np.float32): + """Initializes WrappedPrioritizedReplayBuffer. + + Args: + observation_shape: tuple of ints. + stack_size: int, number of frames to use in state stack. + use_staging: bool, when True it would use a staging area to prefetch + the next sampling batch. + replay_capacity: int, number of transitions to keep in memory. + batch_size: int. + update_horizon: int, length of update ('n' in n-step update). + gamma: int, the discount factor. + max_sample_attempts: int, the maximum number of attempts allowed to + get a sample. + extra_storage_types: list of ReplayElements defining the type of the extra + contents that will be stored and returned by sample_transition_batch. + observation_dtype: np.dtype, type of the observations. Defaults to + np.uint8 for Atari 2600. + action_shape: tuple of ints, the shape for the action vector. Empty tuple + means the action is a scalar. + action_dtype: np.dtype, type of elements in the action. + reward_shape: tuple of ints, the shape of the reward vector. Empty tuple + means the reward is a scalar. + reward_dtype: np.dtype, type of elements in the reward. + + Raises: + ValueError: If update_horizon is not positive. + ValueError: If discount factor is not in [0, 1]. + """ + memory = OutOfGraphPrioritizedReplayBuffer( + observation_shape, stack_size, replay_capacity, batch_size, + update_horizon, gamma, max_sample_attempts, + extra_storage_types=extra_storage_types, + observation_dtype=observation_dtype) + super(WrappedPrioritizedReplayBuffer, self).__init__( + observation_shape, + stack_size, + use_staging, + replay_capacity, + batch_size, + update_horizon, + gamma, + wrapped_memory=memory, + extra_storage_types=extra_storage_types, + observation_dtype=observation_dtype, + action_shape=action_shape, + action_dtype=action_dtype, + reward_shape=reward_shape, + reward_dtype=reward_dtype) + + def tf_set_priority(self, indices, priorities): + """Sets the priorities for the given indices. + + Args: + indices: tf.Tensor with dtype int32 and shape [n]. + priorities: tf.Tensor with dtype float and shape [n]. + + Returns: + A tf op setting the priorities for prioritized sampling. + """ + return tf.py_func( + self.memory.set_priority, [indices, priorities], [], + name='prioritized_replay_set_priority_py_func') + + def tf_get_priority(self, indices): + """Gets the priorities for the given indices. + + Args: + indices: tf.Tensor with dtype int32 and shape [n]. + + Returns: + priorities: tf.Tensor with dtype float and shape [n], the priorities at + the indices. + """ + return tf.py_func( + self.memory.get_priority, [indices], + tf.float32, + name='prioritized_replay_get_priority_py_func') diff --git a/dopamine/replay_memory/sum_tree.py b/dopamine/replay_memory/sum_tree.py index 406a491..c9fdff5 100644 --- a/dopamine/replay_memory/sum_tree.py +++ b/dopamine/replay_memory/sum_tree.py @@ -1,205 +1,205 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""A sum tree data structure. - -Used for prioritized experience replay. See prioritized_replay_buffer.py -and Schaul et al. (2015). -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import math -import random - -import numpy as np - - -class SumTree(object): - """A sum tree data structure for storing replay priorities. - - A sum tree is a complete binary tree whose leaves contain values called - priorities. Internal nodes maintain the sum of the priorities of all leaf - nodes in their subtree. - - For capacity = 4, the tree may look like this: - - +---+ - |2.5| - +-+-+ - | - +-------+--------+ - | | - +-+-+ +-+-+ - |1.5| |1.0| - +-+-+ +-+-+ - | | - +----+----+ +----+----+ - | | | | - +-+-+ +-+-+ +-+-+ +-+-+ - |0.5| |1.0| |0.5| |0.5| - +---+ +---+ +---+ +---+ - - This is stored in a list of numpy arrays: - self.nodes = [ [2.5], [1.5, 1], [0.5, 1, 0.5, 0.5] ] - - For conciseness, we allocate arrays as powers of two, and pad the excess - elements with zero values. - - This is similar to the usual array-based representation of a complete binary - tree, but is a little more user-friendly. - """ - - def __init__(self, capacity): - """Creates the sum tree data structure for the given replay capacity. - - Args: - capacity: int, the maximum number of elements that can be stored in this - data structure. - - Raises: - ValueError: If requested capacity is not positive. - """ - assert isinstance(capacity, int) - if capacity <= 0: - raise ValueError('Sum tree capacity should be positive. Got: {}'. - format(capacity)) - - self.nodes = [] - tree_depth = int(math.ceil(np.log2(capacity))) - level_size = 1 - for _ in range(tree_depth + 1): - nodes_at_this_depth = np.zeros(level_size) - self.nodes.append(nodes_at_this_depth) - - level_size *= 2 - - self.max_recorded_priority = 1.0 - - def _total_priority(self): - """Returns the sum of all priorities stored in this sum tree. - - Returns: - float, sum of priorities stored in this sum tree. - """ - return self.nodes[0][0] - - def sample(self, query_value=None): - """Samples an element from the sum tree. - - Each element has probability p_i / sum_j p_j of being picked, where p_i is - the (positive) value associated with node i (possibly unnormalized). - - Args: - query_value: float in [0, 1], used as the random value to select a - sample. If None, will select one randomly in [0, 1). - - Returns: - int, a random element from the sum tree. - - Raises: - Exception: If the sum tree is empty (i.e. its node values sum to 0), or if - the supplied query_value is larger than the total sum. - """ - if self._total_priority() == 0.0: - raise Exception('Cannot sample from an empty sum tree.') - - if query_value and (query_value < 0. or query_value > 1.): - raise ValueError('query_value must be in [0, 1].') - - # Sample a value in range [0, R), where R is the value stored at the root. - query_value = random.random() if query_value is None else query_value - query_value *= self._total_priority() - - # Now traverse the sum tree. - node_index = 0 - for nodes_at_this_depth in self.nodes[1:]: - # Compute children of previous depth's node. - left_child = node_index * 2 - - left_sum = nodes_at_this_depth[left_child] - # Each subtree describes a range [0, a), where a is its value. - if query_value < left_sum: # Recurse into left subtree. - node_index = left_child - else: # Recurse into right subtree. - node_index = left_child + 1 - # Adjust query to be relative to right subtree. - query_value -= left_sum - - return node_index - - def stratified_sample(self, batch_size): - """Performs stratified sampling using the sum tree. - - Let R be the value at the root (total value of sum tree). This method will - divide [0, R) into batch_size segments, pick a random number from each of - those segments, and use that random number to sample from the sum_tree. This - is as specified in Schaul et al. (2015). - - Args: - batch_size: int, the number of strata to use. - Returns: - list of batch_size elements sampled from the sum tree. - - Raises: - Exception: If the sum tree is empty (i.e. its node values sum to 0). - """ - if self._total_priority() == 0.0: - raise Exception('Cannot sample from an empty sum tree.') - - bounds = np.linspace(0., 1., batch_size + 1) - assert len(bounds) == batch_size + 1 - segments = [(bounds[i], bounds[i+1]) for i in range(batch_size)] - query_values = [random.uniform(x[0], x[1]) for x in segments] - return [self.sample(query_value=x) for x in query_values] - - def get(self, node_index): - """Returns the value of the leaf node corresponding to the index. - - Args: - node_index: The index of the leaf node. - Returns: - The value of the leaf node. - """ - return self.nodes[-1][node_index] - - def set(self, node_index, value): - """Sets the value of a leaf node and updates internal nodes accordingly. - - This operation takes O(log(capacity)). - Args: - node_index: int, the index of the leaf node to be updated. - value: float, the value which we assign to the node. This value must be - nonnegative. Setting value = 0 will cause the element to never be - sampled. - - Raises: - ValueError: If the given value is negative. - """ - if value < 0.0: - raise ValueError('Sum tree values should be nonnegative. Got {}'. - format(value)) - self.max_recorded_priority = max(value, self.max_recorded_priority) - - delta_value = value - self.nodes[-1][node_index] - - # Now traverse back the tree, adjusting all sums along the way. - for nodes_at_this_depth in reversed(self.nodes): - # Note: Adding a delta leads to some tolerable numerical inaccuracies. - nodes_at_this_depth[node_index] += delta_value - node_index //= 2 - - assert node_index == 0, ('Sum tree traversal failed, final node index ' - 'is not 0.') +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""A sum tree data structure. + +Used for prioritized experience replay. See prioritized_replay_buffer.py +and Schaul et al. (2015). +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import random + +import numpy as np + + +class SumTree(object): + """A sum tree data structure for storing replay priorities. + + A sum tree is a complete binary tree whose leaves contain values called + priorities. Internal nodes maintain the sum of the priorities of all leaf + nodes in their subtree. + + For capacity = 4, the tree may look like this: + + +---+ + |2.5| + +-+-+ + | + +-------+--------+ + | | + +-+-+ +-+-+ + |1.5| |1.0| + +-+-+ +-+-+ + | | + +----+----+ +----+----+ + | | | | + +-+-+ +-+-+ +-+-+ +-+-+ + |0.5| |1.0| |0.5| |0.5| + +---+ +---+ +---+ +---+ + + This is stored in a list of numpy arrays: + self.nodes = [ [2.5], [1.5, 1], [0.5, 1, 0.5, 0.5] ] + + For conciseness, we allocate arrays as powers of two, and pad the excess + elements with zero values. + + This is similar to the usual array-based representation of a complete binary + tree, but is a little more user-friendly. + """ + + def __init__(self, capacity): + """Creates the sum tree data structure for the given replay capacity. + + Args: + capacity: int, the maximum number of elements that can be stored in this + data structure. + + Raises: + ValueError: If requested capacity is not positive. + """ + assert isinstance(capacity, int) + if capacity <= 0: + raise ValueError('Sum tree capacity should be positive. Got: {}'. + format(capacity)) + + self.nodes = [] + tree_depth = int(math.ceil(np.log2(capacity))) + level_size = 1 + for _ in range(tree_depth + 1): + nodes_at_this_depth = np.zeros(level_size) + self.nodes.append(nodes_at_this_depth) + + level_size *= 2 + + self.max_recorded_priority = 1.0 + + def _total_priority(self): + """Returns the sum of all priorities stored in this sum tree. + + Returns: + float, sum of priorities stored in this sum tree. + """ + return self.nodes[0][0] + + def sample(self, query_value=None): + """Samples an element from the sum tree. + + Each element has probability p_i / sum_j p_j of being picked, where p_i is + the (positive) value associated with node i (possibly unnormalized). + + Args: + query_value: float in [0, 1], used as the random value to select a + sample. If None, will select one randomly in [0, 1). + + Returns: + int, a random element from the sum tree. + + Raises: + Exception: If the sum tree is empty (i.e. its node values sum to 0), or if + the supplied query_value is larger than the total sum. + """ + if self._total_priority() == 0.0: + raise Exception('Cannot sample from an empty sum tree.') + + if query_value and (query_value < 0. or query_value > 1.): + raise ValueError('query_value must be in [0, 1].') + + # Sample a value in range [0, R), where R is the value stored at the root. + query_value = random.random() if query_value is None else query_value + query_value *= self._total_priority() + + # Now traverse the sum tree. + node_index = 0 + for nodes_at_this_depth in self.nodes[1:]: + # Compute children of previous depth's node. + left_child = node_index * 2 + + left_sum = nodes_at_this_depth[left_child] + # Each subtree describes a range [0, a), where a is its value. + if query_value < left_sum: # Recurse into left subtree. + node_index = left_child + else: # Recurse into right subtree. + node_index = left_child + 1 + # Adjust query to be relative to right subtree. + query_value -= left_sum + + return node_index + + def stratified_sample(self, batch_size): + """Performs stratified sampling using the sum tree. + + Let R be the value at the root (total value of sum tree). This method will + divide [0, R) into batch_size segments, pick a random number from each of + those segments, and use that random number to sample from the sum_tree. This + is as specified in Schaul et al. (2015). + + Args: + batch_size: int, the number of strata to use. + Returns: + list of batch_size elements sampled from the sum tree. + + Raises: + Exception: If the sum tree is empty (i.e. its node values sum to 0). + """ + if self._total_priority() == 0.0: + raise Exception('Cannot sample from an empty sum tree.') + + bounds = np.linspace(0., 1., batch_size + 1) + assert len(bounds) == batch_size + 1 + segments = [(bounds[i], bounds[i+1]) for i in range(batch_size)] + query_values = [random.uniform(x[0], x[1]) for x in segments] + return [self.sample(query_value=x) for x in query_values] + + def get(self, node_index): + """Returns the value of the leaf node corresponding to the index. + + Args: + node_index: The index of the leaf node. + Returns: + The value of the leaf node. + """ + return self.nodes[-1][node_index] + + def set(self, node_index, value): + """Sets the value of a leaf node and updates internal nodes accordingly. + + This operation takes O(log(capacity)). + Args: + node_index: int, the index of the leaf node to be updated. + value: float, the value which we assign to the node. This value must be + nonnegative. Setting value = 0 will cause the element to never be + sampled. + + Raises: + ValueError: If the given value is negative. + """ + if value < 0.0: + raise ValueError('Sum tree values should be nonnegative. Got {}'. + format(value)) + self.max_recorded_priority = max(value, self.max_recorded_priority) + + delta_value = value - self.nodes[-1][node_index] + + # Now traverse back the tree, adjusting all sums along the way. + for nodes_at_this_depth in reversed(self.nodes): + # Note: Adding a delta leads to some tolerable numerical inaccuracies. + nodes_at_this_depth[node_index] += delta_value + node_index //= 2 + + assert node_index == 0, ('Sum tree traversal failed, final node index ' + 'is not 0.') diff --git a/dopamine/utils/__init__.py b/dopamine/utils/__init__.py index 920cbb5..dc108ba 100644 --- a/dopamine/utils/__init__.py +++ b/dopamine/utils/__init__.py @@ -1,15 +1,15 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/dopamine/utils/test_utils.py b/dopamine/utils/test_utils.py index 05ecd73..694f91b 100644 --- a/dopamine/utils/test_utils.py +++ b/dopamine/utils/test_utils.py @@ -1,34 +1,34 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Common testing utilities shared across agents.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - - -import mock -import tensorflow as tf - - -class MockReplayBuffer(object): - """Mock ReplayBuffer to verify the way the agent interacts with it.""" - - def __init__(self): - with tf.variable_scope('MockReplayBuffer', reuse=tf.AUTO_REUSE): - self.add = mock.Mock() - self.memory = mock.Mock() - self.memory.add_count = 0 +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Common testing utilities shared across agents.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + + +import mock +import tensorflow as tf + + +class MockReplayBuffer(object): + """Mock ReplayBuffer to verify the way the agent interacts with it.""" + + def __init__(self): + with tf.variable_scope('MockReplayBuffer', reuse=tf.AUTO_REUSE): + self.add = mock.Mock() + self.memory = mock.Mock() + self.memory.add_count = 0 diff --git a/setup.py b/setup.py index 8328f3a..b755efa 100644 --- a/setup.py +++ b/setup.py @@ -1,92 +1,92 @@ -# coding=utf-8 -# Copyright 2018 The Dopamine Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Setup script for Dopamine. - -This script will install Dopamine as a Python module. - -See: https://github.com/google/dopamine - -""" - -import codecs -from os import path -from setuptools import find_packages -from setuptools import setup - -here = path.abspath(path.dirname(__file__)) - -# Get the long description from the README file. -with codecs.open(path.join(here, 'README.md'), encoding='utf-8') as f: - long_description = f.read() - -install_requires = ['gin-config == 0.1.4', 'absl-py >= 0.2.2', - 'opencv-python >= 3.4.1.15', - 'gym >= 0.10.5'] -tests_require = ['gin-config >= 0.1.1', 'absl-py >= 0.2.2', - 'opencv-python >= 3.4.1.15', - 'gym >= 0.10.5', 'mock >= 1.0.0'] - -dopamine_description = ( - 'Dopamine: A framework for flexible Reinforcement Learning research') - -setup( - name='dopamine_rl', - version='2.0.1', - include_package_data=True, - packages=find_packages(exclude=['docs']), # Required - package_data={'testdata': ['testdata/*.gin']}, - install_requires=install_requires, - tests_require=tests_require, - description=dopamine_description, - long_description=long_description, - url='https://github.com/google/dopamine', # Optional - author='The Dopamine Team', # Optional - author_email='opensource@google.com', - classifiers=[ # Optional - 'Development Status :: 4 - Beta', - - # Indicate who your project is intended for - 'Intended Audience :: Developers', - 'Intended Audience :: Education', - 'Intended Audience :: Science/Research', - - # Pick your license as you wish - 'License :: OSI Approved :: Apache Software License', - - # Specify the Python versions you support here. In particular, ensure - # that you indicate whether you support Python 2, Python 3 or both. - 'Programming Language :: Python :: 2', - 'Programming Language :: Python :: 2.7', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.4', - 'Programming Language :: Python :: 3.5', - 'Programming Language :: Python :: 3.6', - - 'Topic :: Scientific/Engineering', - 'Topic :: Scientific/Engineering :: Mathematics', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - 'Topic :: Software Development', - 'Topic :: Software Development :: Libraries', - 'Topic :: Software Development :: Libraries :: Python Modules', - - ], - project_urls={ # Optional - 'Documentation': 'https://github.com/google/dopamine', - 'Bug Reports': 'https://github.com/google/dopamine/issues', - 'Source': 'https://github.com/google/dopamine', - }, - license='Apache 2.0', - keywords='dopamine reinforcement-learning python machine learning' -) +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Setup script for Dopamine. + +This script will install Dopamine as a Python module. + +See: https://github.com/google/dopamine + +""" + +import codecs +from os import path +from setuptools import find_packages +from setuptools import setup + +here = path.abspath(path.dirname(__file__)) + +# Get the long description from the README file. +with codecs.open(path.join(here, 'README.md'), encoding='utf-8') as f: + long_description = f.read() + +install_requires = ['gin-config == 0.1.4', 'absl-py >= 0.2.2', + 'opencv-python >= 3.4.1.15', + 'gym >= 0.10.5'] +tests_require = ['gin-config >= 0.1.1', 'absl-py >= 0.2.2', + 'opencv-python >= 3.4.1.15', + 'gym >= 0.10.5', 'mock >= 1.0.0'] + +dopamine_description = ( + 'Dopamine: A framework for flexible Reinforcement Learning research') + +setup( + name='dopamine_rl', + version='2.0.1', + include_package_data=True, + packages=find_packages(exclude=['docs']), # Required + package_data={'testdata': ['testdata/*.gin']}, + install_requires=install_requires, + tests_require=tests_require, + description=dopamine_description, + long_description=long_description, + url='https://github.com/google/dopamine', # Optional + author='The Dopamine Team', # Optional + author_email='opensource@google.com', + classifiers=[ # Optional + 'Development Status :: 4 - Beta', + + # Indicate who your project is intended for + 'Intended Audience :: Developers', + 'Intended Audience :: Education', + 'Intended Audience :: Science/Research', + + # Pick your license as you wish + 'License :: OSI Approved :: Apache Software License', + + # Specify the Python versions you support here. In particular, ensure + # that you indicate whether you support Python 2, Python 3 or both. + 'Programming Language :: Python :: 2', + 'Programming Language :: Python :: 2.7', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.4', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', + + 'Topic :: Scientific/Engineering', + 'Topic :: Scientific/Engineering :: Mathematics', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Software Development', + 'Topic :: Software Development :: Libraries', + 'Topic :: Software Development :: Libraries :: Python Modules', + + ], + project_urls={ # Optional + 'Documentation': 'https://github.com/google/dopamine', + 'Bug Reports': 'https://github.com/google/dopamine/issues', + 'Source': 'https://github.com/google/dopamine', + }, + license='Apache 2.0', + keywords='dopamine reinforcement-learning python machine learning' +)