This commit is contained in:
Mehdi Fatemi 2019-10-29 12:11:02 -04:00
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Коммит 65a74230a2
13 изменённых файлов: 114 добавлений и 1 удалений

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CONTRIBUTING.md Normal file
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# 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.

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MIT License
LOGARITHMIC REINFORCEMENT LEARNING
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy

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NOTICE.md Normal file
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## OSS License Notice
The files in the `log_dqn_experiments/log_dqn
` folder are mostly derived from [Dopamine](https://github.com/google/dopamine). Please see the original license [here](https://github.com/google/dopamine/blob/master/LICENSE).

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We provide code for the linear experiments of the paper as well as the deep RL Atari 2600 examples (LogDQN).
## For the license, please see [LICENSE](https://github.com/microsoft/logrl/blob/master/LICENSE).
## [LICENSE](https://github.com/microsoft/logrl/blob/master/LICENSE)
## [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
The code for LogDQN has been developed by [Arash Tavakoli](https://atavakol.github.io/) and the code for the linear experiments has been developed by [Harm van Seijen](mailto:Harm.vanSeijen@microsoft.com).

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
import numpy as np
class Agent(object):

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
import numpy as np
import math

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
import numpy as np
import json
import time

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
import numpy as np
import math
import json

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
name = "log_dqn"

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
# This file is derived from Dopamine with the following original copyright note:
# 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 modified to support float64 rewards.
This script modifies the Dopamine's implementation of an out-of-graph

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
# This file is partially derived from Dopamine with the following original copyright note:
# 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 LogDQN agent.
Details in "Using a Logarithmic Mapping to Enable Lower Discount Factors

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
# This file is partially derived from Dopamine with the following original copyright note:
# 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 an agent on an Atari 2600 domain.
This script modifies Dopamine's `train.py` to support LogDQN.

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'''
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
'''
import codecs
from os import path
from setuptools import find_packages