lightATAC
This is a lightweight reimplementation of Adversarially Trained Actor Critic (ATAC), a model-free offline reinforcement learning algorithm by Ching-An Cheng*, Tengyang Xie*, Nan Jiang, and Alekh Agarwal.
To install, simply clone the repo and run pip install -e . . It uses mujoco210, which can be installed, if needed, following the commands below.
bash install.sh
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mujoco210/bin:/usr/lib/nvidia" >> ~/.bashrc
source ~/.bashrc
Then you can start the training by running, e.g.,
python main.py --log_dir ./tmp_results --env_name hopper-medium-expert-v2 --beta 1.0
More instructions can be found in main.py, and please see the original paper for hyperparameters (e.g., beta). The code was tested with python 3.9.
The experimental results of lightATAC (over different
This reimplementation is based on gwthomas/IQL-PyTorch. It is minimalistic, so users can easily modify it for their needs. It follows mostly the logic in the original ATAC code, but with some code optimization, which gives about 1.5X-2X speed up.
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.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Trademarks
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