Reinforcement learning library in JAX.

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Deep Learningmachine-learningresearchreinforcement-learningjax
Overview

Magi RL library in JAX

Installation | Agents | Examples | Contributing | Documentation

pytest Code style: black

Magi is a RL library developed on top of Acme.

Note: Magi is in alpha development so expect breaking changes!

Installation

  1. Create a new Python virtual environment
python3 -m venv venv
source venv/bin/activate
  1. Install dependencies and the package in editable mode by running
pip install -U pip setuptools wheel
pip install -r requirements.txt # This uses pinned dependencies, you may adjust this for your needs.
pip install -e .

If for some reason installation fails, first check out GitHub Actions badge to see if this fails on the latest CI run. If the CI is successful, then it's likely that there are some issues to setting up your own environment. Refer to .github/workflows/ci.yaml as the official source for how to set up the environment.

Agents

magi includes popular RL algorithm implementation such as SAC, DrQ, SAC-AE and PETS. Refer to magi/agents for a full list of agents.

Examples

Check out magi/examples where we include examples of using our RL agents on popular benchmark tasks.

Testing

On Linux, you can run tests with

JAX_PLATFORM_NAME=cpu pytest -n `grep -c ^processor /proc/cpuinfo` magi

Contributing

Refer to CONTRIBUTING.md.

Acknowledgements

Magi is inspired by many of the open-source RL projects out there. Here is a (non-exhaustive) list of related libraries and packages that Magi references:

License

Apache License 2.0

Citation

If you use Magi in your work, please cite us according to the CITATION file. You may learn more about the CITATION file from here.

Owner
Yicheng Luo
PhD student at UCL AI Center. Former intern at @deepmind and @secondmind-labs.
Yicheng Luo
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