Reinforcement Learning-Based MAS Interception in Antagonistic Environments  

作　　者：Siqing Sun Defu Cai Hai-Tao Zhang Ning Xing 

机构地区：[1]the School of Artificial Intelligence and Automation,the MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems,the Guangdong Engineering Technology Research Center of Fully Autonomous Unmanned Surface Vehicles,Huazhong University of Science and Technology,Wuhan 430074,China [2]the State Grid Hubei Electric Power Research Institute,Wuhan 430074,China [3]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第1期270-272,共3页自动化学报（英文版）

基　　金：supported by the Science and Technology Project of State Grid Corporation of China, China (5100202199557A-0-5-ZN)。

摘　　要：Dear Editor, As a promising multi-agent systems(MASs) operation, autonomous interception has attracted more and more attentions in these years, where defenders prevent intruders from reaching destinations.So far, most of the relevant methods are applied in ideal environments without agent damages. As a remedy, this letter proposes a more realistic interception method for MASs suffered by damages.

关 键 词：AGENT MAS DESTINATION 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249[自动化与计算机技术—控制理论与控制工程] TP181[自动化与计算机技术—控制科学与工程]