Safe Reinforcement Learning with Stability Guarantees
This code accompanies the paper [1] and implements the code for estimating the region of attraction for a policy and optimizing the policy subject to stability constraints. For the old numpy-based code to estimate the region of attraction in [2] see the lyapunov-learning repository.
| [1] | F. Berkenkamp, M. Turchetta, A. P. Schoellig, A. Krause, Safe Model-based Reinforcement Learning with Stability Guarantees in Proc. of the Conference on Neural Information Processing Systems (NIPS), 2017. |
| [2] | F. Berkenkamp, R. Moriconi, A. P. Schoellig, A. Krause, Safe Learning of Regions of Attraction in Uncertain, Nonlinear Systems with Gaussian Processes <http://arxiv.org/abs/1603.04915> in Proc. of the Conference on Decision and Control (CDC), 2016. |
Getting started
You can install the library by cloning the repository and installing it with
pip install .
You can the find example jupyter notebooks and the experiments in the paper in the examples folder.
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