Efficient Neural Lyapunov Function Approximation with Reinforcement Learning

NOTE

The previous SACLA codebase is being migrated here and therefore there are some files etc missing. We are hoping to finish this by May 2025, however in the meantime feel free to reach out to [email protected] with any questions

Installation instructions

1. clone repo with https://github.com/CAV-Research-Lab/SACLA.git
2. Install requirements via pip install -e . when in the root folder. This will install all the requirements to build the prob_lyap package from the pyproject.toml file.
3. run main.py --help to see configuration options.

We recommend using environments from the Gymnasium robotics benchmark or InvertedPendulum-v4 however you can easily modify the chosen equilibrium state in lyap_func_InvertedPendulum.py to extend to other non goal-conditioned environments.

There should be some updates coming soon as we refactor the codebase! If you would like to collaberate or have any suggestions please feel free to reach out.

If you would like to cite this work please use the following: @ X