coax 0.1.5

Plug-n-play reinforcement learning with OpenAI Gym and JAX

coax

Plug-n-Play Reinforcement Learning in Python with OpenAI Gym and JAX

For the full documentation, including many examples, go to https://coax.readthedocs.io/

Install

coax is built on top of JAX, but it doesn’t have an explicit dependence on the jax python package. The reason is that your version of jaxlib will depend on your CUDA version. To install without CUDA, simply run:

$ pip install jaxlib jax coax --upgrade

If you do require CUDA support, please check out the Installation Guide.

Getting Started

Have a look at the Getting Started page to train your first RL agent.

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 [e-mail] with any additional questions or comments.

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