Improved Event-triggered Approximate Optimal Control for Nonlinear Nonzero-sum Games Using Reinforcement Learning

This paper presents event-triggered integral re-inforcement learning methods to solve nonlinear nonzero-sum differential game problems. Firstly, for nonlinear systems, by constructing coupled Hamilton-Jacobi equations, the theoretical basis for solving multi-player nonzero-sum game problems is established. With the help of integral reinforcement learning, the approximate optimal control strategy corresponding to each player can be obtained without knowing the drift dynamics of the system. Then, the event-triggered mechanism with preliminary operation is constructed by designing appropriate triggering condition. The dynamic triggering mechanism is further integrated into the algorithm architecture of online learning method to realize aperiodic adaptive learning and sampling control, and effectively save system computing and communication resources. Finally, the effectiveness of the proposed reinforcement learning method is verified by theory analyses and simulation experiments.