Discrete Event Modeling and Simulation for Reinforcement Learning System Design

Discrete event modeling and simulation reinforcement learning are two frameworks suited for cyberphysical system design, which, when combined, can give powerful tools optimization or decision making process example. This paper describes how discrete could be integrated into concepts in order to assist the realization of systems, more specially considering temporal, hierarchical, multi-agent aspects. An overview these different improvements given based on implementation Q-Learning algorithm framework Event Specification (DEVS) System Entity Structure (SES) formalisms.