Distributed Model-Free Optimal Control for Multiagent Pursuit-Evasion Differential Games

作     者：Zhang, Huaipin Zhao, Wei Ge, Hui Xie, Xiangpeng Yue, Dong 

作者机构：Nanjing Univ Posts & Telecommun Inst Adv Technol Carbon Neutral Nanjing 210023 Peoples R China Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING》 (IEEE Trans. Netw. Sci. Eng.)

年 卷 期：2024年第11卷第4期

页      面：3800-3811页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0701[理学-数学] 

基　　金：National Key R&D Program of China 

主　　题：Games Transform coding Differential games Nash equilibrium Topology Symmetric matrices Sensors Multiagent systems pursuit-evasion games differential graphical games reinforcement learning 

摘      要：This paper designs optimal control polices for networked multiagent pursuit-evasion game (MPEG) problems based on reinforcement learning (RL) technique. Depending on the number of evaders, MPEG is formulated into several simpler multiple-pursuer single-evader games (MPSEGs) by a divide and conquer approach. Then we propose optimal control policies for all the agents in each MPSEG, which constitute a distributed Nash equilibrium, and provide the capturability and Nash equilibrium analysis. Finally, a data-driven RL algorithm is developed to online learn optimal control polices using measurable behavior data. A simulation example is given to verify the effectiveness of the proposed approach.