MASA Safe RL Docs

Welcome

Welcome to MASA-Safe-RL, the Multi and Single Agent (MASA) Safe Reinforcement Learning library. The primary goal of this library is to develop a set of common constraints and environments for safe reinforcement learning research, built on top of the popular Gymnasium API. We span, CMPDs, probabilistic constraints, Reach-Avoid and LTL-Safety (DFA) properties.

The library is in very early stage development and we greatly appreciate and encourage feedback from the community about what they would like to see implemented. Currently we provide a set of basic tabular algroithms for safe RL, but we provide a modular and resuable framework for developing more complex algorithms and constraints.

Organization

The docs are currently organised as follows:

  • Get Started: installation instructions and core concepts (e.g., labelling function, cost function, make_env).

  • Common API: API references for common constraints, wrappers, metrics logging and temporal logics used in MASA.

  • Environments: list of benchmark environments currently provided in MASA.

  • Algorithms: list of algorithms currently provided in MASA.

  • Misc: auxilliary wrappers, e.g., probabilistic shielding.

We recommend you follow the docs in the provided order starting from Get Started to learn about the core concepts in MASA and basic usage.

Citation

If you use MASA in your research please cite it in your publications.

@misc{Goodall2025MASASafeRL,
  title        = {{MASA-Safe-RL}: Multi and Single Agent Safe Reinforcement Learning},
  author       = {Goodall, Alexander W. and Adalat, Omar and Hamel De-le Court, Edwin and Belardinelli, Francesco},
  year         = {2025},
  howpublished = {\url{https://github.com/sacktock/MASA-Safe-RL/}},
  note         = {GitHub repository}
}

Next Steps