空间控制技术与应用2025，Vol.51Issue(4)：17-28,12.DOI:10.3969/j.issn.1674-1579.2025.04.002
多智能体强化学习赋能空间无人系统:方法、挑战与机遇
Multi-Agent Reinforcement Learning Empower Space Unmanned Systems:Methods,Challenges and Opportunities
摘要
Abstract
With the advancement of space technology towards intelligence and clusterisation,unmanned space systems demonstrate immense potential in strategic areas such as deep space exploration and Earth observation.However,traditional centralized control paradigms face significant challenges in adressing highly dynamic environments,distributed tasks,and strict resource constraints.Leveraging its distributed decision-making architecture and co-evolutionary mechanisms,multi-agent reinforcement learning(MARL)offers a breakthrough solution for building autonomous and resilient intelligent space systems.This paper systematically explores MARL's technological empowerment pathways,methodologies,engineering challenges,and opportunities in unmanned space systems.It analyzes the technical bottlenecks in core scenarios(e.g.,collaborative communication for satellite clusters,multi-spacecraft control).Moreover,It reveals the application mechanisms of MARL in critical domains,including dynamic spectrum allocation,on-board edge computing,and robust collaborative control.Finally,the paper proposes an integrated collaborative intelligence architecture that incorporates space-dynamics constraints with innovative MARL algorithms.This framework aims to drive the evolution of space systems toward a new paradigm of autonomous decision-making,resilient anti-jamming capabilities,and efficient collaboration.This research seeks to provide theoretical support and a technological roadmap for the next-generation space-based intelligent networks.关键词
多智能体强化学习/空间无人系统/协同控制/边缘计算/自主决策Key words
multi-agent reinforcement learning(MARL)/unmanned space systems/collaborative control/edge computing/autonomous decision-making分类
航空航天引用本文复制引用
李勐,冯肇晗,梅云鹏,曹宏杰,张博,王钢..多智能体强化学习赋能空间无人系统:方法、挑战与机遇[J].空间控制技术与应用,2025,51(4):17-28,12.基金项目
国家自然科学基金资助项目(U23B2059) National Natural Science Foundation of China(U23B2059) （U23B2059）
