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Stabilizing Neural Control Using Self-Learned Almost Lyapunov Critics
[article]
2021
arXiv
pre-print
The lack of stability guarantee restricts the practical use of learning-based methods in core control problems in robotics. We develop new methods for learning neural control policies and neural Lyapunov critic functions in the model-free reinforcement learning (RL) setting. We use sample-based approaches and the Almost Lyapunov function conditions to estimate the region of attraction and invariance properties through the learned Lyapunov critic functions. The methods enhance stability of
arXiv:2107.04989v1
fatcat:aq6rpkuf65bk3k4gdci2uzoara