通信学报2026，Vol.47Issue(1)：141-155,15.DOI:10.11959/j.issn.1000−436x.2026005
基于多智能体深度强化学习的智能网联汽车服务迁移优化方法
Service migration optimization method for intelligent connected vehicles based on multi-agent deep reinforcement learning
摘要
Abstract
To address the challenges of multi-user resource competition and dynamic changes in edge node availability faced by intelligent connected vehicles during service migration in a highly dynamic Internet of vehicles environment,a service migration method based on multi-agent group relative policy optimization(MAGRPO)was proposed.The ser-vice migration problem was formalized as a long-term multi-user joint optimization problem with resource constraints,and a MAGRPO algorithm that did not require an explicit critic network was designed.A policy update signal was con-structed based on the relative ranking of discounted returns within the group,thereby effectively mitigating training insta-bility caused by severe penalties(e.g.,node overload or failure)and reducing training cost.Simulation results show that the proposed method outperforms existing baseline methods in key metrics such as total service delay,migration energy consumption,and migration success rate.It exhibits stronger robustness and scalability in scenarios where edge node re-sources are limited and their availability changes dynamically.关键词
移动边缘计算/智能网联汽车/服务迁移/多智能体深度强化学习/组相对策略优化Key words
mobile edge computing/intelligent connected vehicles/service migration/multi-agent deep reinforcement learning/group relative policy optimization分类
信息技术与安全科学引用本文复制引用
芮兰兰,邓淑予,陈子轩,高志鹏,邱雪松,郭少勇..基于多智能体深度强化学习的智能网联汽车服务迁移优化方法[J].通信学报,2026,47(1):141-155,15.基金项目
国家自然科学基金资助项目(No.62471051) （No.62471051）
河北省创新能力提升计划基金资助项目(No.V1755673688106)The National Natural Science Foundation of China(No.62471051),Hebei Provincial Innovation Capacity En-hancement Program Project(No.V1755673688106) （No.V1755673688106）
