Integral Reinforcement Learning-Based Dynamic Event-Triggered Nonzero-Sum Games of USVs

作者：Shan Xue, Weidong Zhang, Biao Luo, Derong Liu · 发表于：IEEE Transactions on Cybernetics · 年份：2025 · DOI：10.1109/tcyb.2025.3533139 · 被引用次数：28 · 研究领域：Game Theory and Applications、Supply Chain and Inventory Management、Extremum Seeking Control Systems

In this article, an integral reinforcement learning (IRL) method is developed for dynamic event-triggered nonzero-sum (NZS) games to achieve the Nash equilibrium of unmanned surface vehicles (USVs) with state and input constraints. Initially, a mapping function is designed to map the state and control of the USV into a safe environment. Subsequently, IRL-based coupled Hamilton-Jacobi equations, which avoid dependence on system dynamics, are derived to solve the Nash equilibrium. To conserve computational resources and reduce network transmission burdens, a static event-triggered control is initially designed, followed by the development of a more flexible dynamic form. Finally, a critic neural network is designed for each player to approximate its value function and control policy. Rigorous proofs are provided for the uniform ultimate boundedness of the state and the weight estimation errors. The effectiveness of the present method is demonstrated through simulation experiments.