magi
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ethanluoyc
Reinforcement learning library in JAX.
Magi RL library in JAX
Installation | Agents | Examples | Contributing | Documentation
Magi is a RL library developed on top of Acme.
NOTE: There's a development version of magi available from the develop
branch that is compatible with the HEAD version of Acme. The main
branch is compatible with the latest version of dm-acme on PyPI.
Installation
- Create a new Python virtual environment
python3 -m venv venv
source venv/bin/activate
- Install dependencies and the package in editable mode by running
pip install -U pip setuptools wheel
pip install -r requirements/base.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:
- https://github.com/deepmind/acme
- https://github.com/ikostrikov/jaxrl
- https://github.com/tensorflow/agents
- https://github.com/rail-berkeley/rlkit
License
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.
ethanluoyc