modified: LICENSE

deleted:    LICENSE.txt
	modified:   README.md

LICENSE

@@ -1,3 +1,5 @@
+    Fully parameterized Quantile Function(FQF) for distributional reinforcement learning
+
     MIT License
 
     Copyright (c) Microsoft Corporation.

LICENSE.txt

@@ -1,23 +0,0 @@
-Fully parameterized Quantile Function(FQF) for distributional reinforcement learning
-
-Copyright (c) Microsoft Corporation.
-
-MIT License
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

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+# Fully parameterized Quantile Function (FQF)
 
-# Contributing
+Tensorflow implementation of paper
 
-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.opensource.microsoft.com.
+**[Fully Parameterized Quantile Function for Distribution Reinforcement Learning](https://arxiv.org/abs/1911.02140)**
 
-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., status check, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
+Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-yan Liu
 
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+
+If you use this code in your research, please cite
+``` tex
+@inproceedings{yang2019fully,
+  title={Fully Parameterized Quantile Function for Distributional Reinforcement Learning},
+  author={Yang, Derek and Zhao, Li and Lin, Zichuan and Qin, Tao and Bian, Jiang and Liu, Tie-Yan},
+  booktitle={Advances in Neural Information Processing Systems},
+  pages={6190--6199},
+  year={2019}
+}
+```
+
+## Requirements
+- python==3.6
+- tensorflow
+- gym
+- absl-py
+- atari-py
+- gin-config
+- opencv-python
+
+## Installation on Ubuntu
+```bash
+sudo apt-get update && sudo apt-get install cmake zlib1g-dev
+pip install absl-py atari-py gin-config==0.1.4 gym opencv-python tensorflow-gpu==1.12.0
+cd FQF
+pip install -e .
+```
+
+## Experiments
+- Our experiments and hyper-parameter searching can be simply run as the following
+```bash
+cd FQF/dopamine/discrete_domains
+bash run-fqf.sh
+```
+
+## Acknowledgement
+- Our code is implemented based on [dopamine](https://github.com/google/dopamine).
+
+
+## Code of Conduct
+- 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.