上海航天（中英文）2025，Vol.42Issue(5)：23-32,10.DOI:10.19328/j.cnki.2096-8655.2025.05.003
基于多智能体深度强化学习的天空地分布式协同卸载方法
An MARL-based Space-air-ground Distributed Collaborative Task Offloading Method
摘要
Abstract
low-Earth-orbit(LEO)satellite constellations,leveraging their characteristics of broad coverage and seamless access,are accelerating the emergence of space-air-ground integrated networks(SAGINs)as a highly promising paradigm for mobile edge computing(MEC).However,existing works have not adequately addressed the critical challenge of task-resource matching in scenarios involving multi-satellite collaborations and dual space-air edges,making efficient offloading of time-and energy-sensitive tasks particularly difficult.This paper formulates a multi-objective joint optimization problem,holistically considering the matching relationship between tasks and multi-dimensional resources,the latency and energy consumption of task processing,and the transmission costs associated with inter-satellite collaborations.A multi-agent reinforcement learning(MARL)-based framework for collaborative multi-task offloading in SAGINs is proposed.This method effectively integrates the cross-domain collaborative decision-making among satellites,unmanned aerial vehicles(UAVs),and ground stations,along with inter-satellite collaborative decision-making.The experimental results demonstrate that the proposed approach achieves efficient convergence and exhibits significant advantages compared with existing methods.关键词
低轨(LEO)卫星星座/跨域协同/星间协同/多智能体强化学习(MARL)/协同卸载Key words
low-Earth-orbit(LEO)satellite constellation/cross-domain/inter-satellite collaboration/multi-agent reinforcement learning(MARL)/collaborative offloading分类
航空航天引用本文复制引用
邱源,孙嘉钰,牛建伟,姚依明,罗翔..基于多智能体深度强化学习的天空地分布式协同卸载方法[J].上海航天（中英文）,2025,42(5):23-32,10.基金项目
国家重点研发计划资助项目(2023YFE0208100) （2023YFE0208100）
