Decentralized Triggering and Event-Based Integral Reinforcement Learning for Multiplayer Differential Game Systems

Abstract

Multiplayer differential games are typically characterized by multiple control loops, where communication resources are periodically transmitted and control policies are updated in a time-triggered manner. In this paper, two different event-triggered mechanisms are proposed for a class of multiplayer nonzero-sum differential game systems. Specifically, by defining a global sampled state, a centralized triggering rule is devised to manage state sampling and control updating in a synchronized manner. By considering each player's preferences, the decentralized triggering rule is devised in which a local event generator produces the triggering sequence independently. On the other hand, with experience replay and integral reinforcement learning, an event-based adaptive learning scheme is developed, which is implemented by critic neural networks and only requires partial knowledge of system dynamics. The theoretical results indicate that both two triggering mechanisms can guarantee the asymptotic stability and weight convergence. Finally, simulation results on a three-player numerical system and a two-player supersonic transport system substantiate the effectiveness of two learning-based triggering mechanisms.