Quick Start

Installation

Python 3.8+ is required but we recommend Python 3.10 (later Python versions may not be supported).

Installation with conda

  • Install conda, e.g., via anaconda.

  • Clone the repo:

git clone https://github.com/sacktock/MASA-Safe-RL.git
cd MASA-Safe-RL

  • Create a conda virtual environment:

conda env create --name masa --file conda-environment.yaml
conda activate masa

  • Install dependencies:

pip install -e .

Installation with uv

uv sync installs the base package from the repository root.

  • For building docs, use uv sync --group docs from the repository root.

  • Adding GPU support for Jax: uv sync --group cuda12 (or cuda13 if supported by your device)

  • All groups (docs and GPU support): uv sync --all-groups.

Installation with PyPI

Coming soon!

Enabling GPU Acceleration with JAX (Optional)

MASA-Safe-RL relies on JAX for GPU acceleration. If you are only interested in the gymnasium wrappers and constraints API then you do not need to complete the following steps.

  • Linux x86_64/aarch64: jax and jaxlib 0.4.30 should already be installed via the requirements.txt. You need to reinstall JAX based on your cuda driver compatibility. Do not use the -U option here!

pip install "jax[cuda13]"

or

pip install "jax[cuda12]"

  • Windows: GPU acceletartion is also supported (experimentally) on Windows WSL x86_64. We strongly recommend using Ubuntu 22.04 or similar. You need to reinstall JAX based on your cuda driver compatibility. Do not use the -U option here!

pip install "jax[cuda13]"

or

pip install "jax[cuda12]"

  • MAC: we recommend JAX with CPU. No further action is required if you correctly followed the earlier steps.

uv

Alternatively with uv,

uv sync --group cuda12

uv sync --group cuda13

Testing Your Installation

You can test your installation of masa via the command line,

python -m masa.run --env-id mini_pacman --algo q_learning --custom-cfgs mini_pacman --seed 0

Next Steps