Optimal supervisory control of discrete event systems based on a reinforcement learning

摘要

This paper proposes a synthesis method of an optimal supervisor based on a reinforcement learning. In discrete event systems a supervisor controls disabling of controllable events to satisfy specifications of the system. The supervisor is usually derived by algorithms based on automaton and language theory. In the proposed algorithm the optimal supervisor is derived under uncertain environment and implicit specifications. By computer simulation we examine an efficiency of the proposed method.