The Stable Learning Control (SLC) framework is a collection of robust Reinforcement Learning control algorithms designed to ensure stability. These algorithms are built upon the Lyapunov actor-critic architecture introduced by Han et al. 2020. They guarantee stability and robustness by leveraging Lyapunov stability theory. These algorithms are specifically tailored for use with gymnasium environments that feature a positive definite cost function. Several ready-to-use compatible environments can be found in the stable-gym package.
Please see the docs for installation and usage instructions.
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Note
We used husky instead of pre-commit, which is more commonly used with Python projects. This was done because only some tools we wanted to use were possible to integrate the Please feel free to open a PR if you want to switch to pre-commit if this is no longer the case.
- Han et al. 2020 - Used as a basis for the Lyapunov actor-critic architecture.
- Spinningup - Used as a basis for the code structure.