多智能体强化学习算法研究综述  

Review of Research on Multi-agent Reinforcement Learning Algorithms

作　　者：李明阳 许可儿 宋志强 夏庆锋 周鹏[1] LI Mingyang;XU Ke’er;SONG Zhiqiang;XIA Qingfeng;ZHOU Peng(School of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Automation,Wuxi University,Wuxi,Jiangsu 214105,China)

机构地区：[1]南京信息工程大学自动化学院,南京210044 [2]无锡学院自动化学院,江苏无锡214105

出　　处：《计算机科学与探索》2024年第8期1979-1997,共19页Journal of Frontiers of Computer Science and Technology

基　　金：江苏省产学研合作项目(BY2021238);无锡学院人才启动经费项目(2021r001)。

摘　　要：近年来,多智能体强化学习算法技术已广泛应用于人工智能领域。系统性地分析了多智能体强化学习算法,审视了其在多智能体系统中的应用与进展,并深入调研了相关研究成果。介绍了多智能体强化学习的研究背景和发展历程,并总结了已有的相关研究成果;简要回顾了传统强化学习算法在不同任务下的应用情况;重点强调多智能体强化学习算法分类,并根据三种主要的任务类型(路径规划、追逃博弈、任务分配)对其在多智能体系统中的应用、挑战以及解决方案进行了细致的梳理与分析;调研了多智能体领域中现有的算法训练环境,总结了深度学习对多智能体强化学习算法的改进作用,提出该领域所面临的挑战并展望了未来的研究方向。In recent years,the technique of multi-agent reinforcement learning algorithm has been widely used in the field of artificial intelligence.This paper systematically analyses the multi-agent reinforcement learning algorithm,examines its application and progress in multi-agent systems,and explores the relevant research results in depth.Firstly,it introduces the research background and development history of multi-agent reinforcement learning and summarizes the existing relevant research results.Secondly,it briefly reviews the application of traditional reinforcement learning algorithms under different tasks.Then,it highlights the classification of multi-agent reinforcement learning algorithms and their application in multi-agent systems according to the three main types of tasks(path planning,pursuit and escape game,task allocation),challenges,and solutions.Finally,it explores the existing algorithm training environments in the field of multi-agents,summarizes the improvement of deep learning on multiagent reinforcement learning algorithms,proposes challenges and looks forward to future research directions in this field.

关 键 词：智能体 强化学习 多智能体强化学习 多智能体系统 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]