计算机工程与应用2025，Vol.61Issue(4)：1-24,24.DOI:10.3778/j.issn.1002-8331.2407-0034
多智能体深度强化学习及可扩展性研究进展
Research Progress on Multi-Agent Deep Reinforcement Learning and Scalability
摘要
Abstract
Multi-agent deep reinforcement learning has shown great potential in solving agent collaboration,competition,and communication problems in recent years.However,as its application expands across more domains,scalability has become a focal concern,which is an important problem from theoretical research to large-scale engineering applications.This paper reviews the reinforcement learning theory and typical algorithms of deep reinforcement learning,introduces three learning paradigms of multi-agent deep reinforcement learning and their representative algorithms,and briefly sum-marizes the current mainstream open-source experimental platforms.Then,this paper delves into the research progress on the scalability of the number and scenarios in multi-agent deep reinforcement learning,analyzes the main problems faced by each method and providing existing solutions.Finally,the application prospect and development trend of multi-agent deep reinforcement learning are prospected,providing references and inspiration to further advance research in this field.关键词
多智能体系统/强化学习/深度强化学习/可扩展性Key words
multi-agent system/reinforcement learning/deep reinforcement learning/scalability分类
信息技术与安全科学引用本文复制引用
刘延飞,李超,王忠,王杰铃..多智能体深度强化学习及可扩展性研究进展[J].计算机工程与应用,2025,61(4):1-24,24.基金项目
国家自然科学基金(U23B2064) （U23B2064）
陕西省杰出青年基金(2021JC-35). （2021JC-35）
