Turn RL papers into code, the easy way
|tests| |pypi| |docs| |License|
Plug-n-Play Reinforcement Learning in Python with
OpenAI Gym_ and
JAX_
|readthedocs|
For the full documentation, including many examples, go to https://coax.readthedocs.io/
coax is built on top of JAX, but it doesn't have an explicit dependence on the
jaxpython package. The reason is that your version of
jaxlibwill depend on your CUDA version. To install without CUDA, simply run:
.. code-block::
$ pip install jaxlib jax coax --upgrade
If you do require CUDA support, please check out the
Installation Guide_.
Have a look at the
Getting Started_ page to train your first RL agent.
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 FAQor contact
[email protected]_ with any additional questions or comments.
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