Stabilizing Neural Control Using Self-Learned Almost Lyapunov Critics [article]

Ya-Chien Chang, Sicun Gao
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
more » ... controllers for various nonlinear systems including automobile and quadrotor control.
arXiv:2107.04989v1 fatcat:aq6rpkuf65bk3k4gdci2uzoara