Modeling and Control of Discrete Event Dynamic Systems: a Simulator-based Reinforcement-learning Paradigm

A general reinforcement-learning approach for controlling discrete event systems is presented. A machine-repair example is formulated: (1) to describe and explain the DEVS formulation, and (2) to illustrate the general control method. Modified gradient learning methods and evolutionary programming methods are compared for the purpose of optimizing the controller. An on-line adaptation method is presented; and, the use of fuzzy logic and artificial neural networks for such adaptation is compared. Evolutionary programming methods for controller optimization prove to be the most robust type of optimization. Moreover, the fuzzy and neural adaptation approaches are successful in improving the performance of the static controller for dynamic operating conditions.