A Learner-verifier Framework for Learning and Certifying Neural Controllers in Stochastic Systems
Reinforcement learning (RL) presents a promising approach to solving highly non-linear control tasks that are often challenging for classical control methods. In this talk, we present a learner-verifier framework for solving control tasks in stochastic systems with formal guarantees on reachability, safety or reach-avoidance specifications. Given a specification and a…
