Learning Stabilizing Policies in Stochastic Control Systems. (arXiv:2205.11991v1 [cs.LG])

In this work, we address the problem of learning provably stable neural
network policies for stochastic control systems. While recent work has
demonstrated the feasibility of certifying given policies using martingale
theory, the problem of how to learn such policies is little explored. Here, we
study the effectiveness of jointly learning a policy together with a martingale
certificate that proves its stability using a single learning algorithm. We
observe that the joint optimization problem becomes easily stuck in local
minima …

arxiv learning stochastic systems