Sampling-based Reactive Synthesis for Nondeterministic Hybrid Systems

This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We view the evolution of the hybrid system as a two-player game, where the nondeterminism is an adversarial player whose objective is to prevent achieving temporal and reachability goals. The aim is to synthesize a winning strategy -- a reactive (robust) strategy that guarantees the satisfaction of the goals under all possible moves of the adversarial player. The approach is based on growing a (search) game-tree in the hybrid space by combining a sampling-based planning method with a novel bandit-based technique to select and improve on partial strategies. We provide conditions under which the algorithm is probabilistically complete, i.e., if a winning strategy exists, the algorithm will almost surely find it. The case studies and benchmark results show that the algorithm is general and consistently outperforms the state of the art.