Physics-informed Reinforcement Learning for Stochastic Reach-Avoid Analysis
A physics-informed RL (PIRL) framework that combines the complementary strengths of PINNs and RL for stochastic reach-avoid analysis, and develops a scheduled PIRL algorithm in which temporal-difference actor-critic learning first guides the critic toward a meaningful approximation of the reach-avoid value function.