Deep Reinforcement Learning or Lyapunov Analysis? A Preliminary Comparative Study on Event-Triggered Optimal Control

Abstract

Dear Editor, This letter develops a novel method to implement event-triggered optimal control (ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning (DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions. Then, dueling double deep Q-network (D3QN) is utilized to achieve our method. In simulations, we present a preliminary comparative study of DRL and Lyapunov analysis for ETOC.