ATAC: Adversarially Trained Actor Critic

Adversarially Trained Actor Critic for Offline Reinforcement Learning by Ching-An Cheng*, Tengyang Xie*, Nan Jiang, and Alekh Agarwal.
https://arxiv.org/abs/2202.02446

Setup

Clone the repository and create a conda environment.

git clone https://github.com/microsoft/ATAC.git
conda create -n atac python=3.8
cd atac

Prerequisite: Install Mujoco

(Optional) Install free mujoco210 for mujoco_py and mujoco211 for dm_control.

bash install_mujoco.sh
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mujoco210/bin:/usr/lib/nvidia" >> ~/.bashrc
source ~/.bashrc

Install ATAC

conda activate atac
pip install -e .[mujoco210]
# or below, if the original paid mujoco is used.
pip install -e .[mujoco200]

Run ATAC

python scripts/main.py

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.opensource.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., 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.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.