空天预警研究学报2026，Vol.40Issue(2)：137-141,156,6.DOI:10.3969/j.issn.2097-180X.2026.02.012
MEC下基于深度强化学习的分布式多目标任务卸载算法
A distributed multi-objective task offloading algorithm based on deep reinforcement learning in MEC
摘要
Abstract
To address the problem that offloading tasks from edge terminals in the mobile edge computing(MEC)scenario can reduce both costs and consumption,but edge nodes are prone to overload because of massive tasks,leading to delay threshold or discarding of partial tasks,this paper proposes a distributed multi-objective task offloading(DMTO)algorithm based on deep reinforcement learning.The proposed algorithm integrates the advantages of the dueling deep Q-network(dueling DQN)and the double deep Q-network(double DQN)to opti-mize the network training efficiency and the decision-making stability.Simulation results show that compared with the mainstream distributed deep reinforcement learning algorithms such as IDDPG,DDTO,DDLO and EDRL,the DMTO algorithm performs better in terms of average delay,energy consumption control and success rate of task offloading.关键词
移动边缘计算/深度强化学习/分布式多目标任务卸载/时延控制Key words
mobile edge computing/deep reinforcement learning/distributed multi-objective task offload-ing/delay control分类
信息技术与安全科学引用本文复制引用
雷军环..MEC下基于深度强化学习的分布式多目标任务卸载算法[J].空天预警研究学报,2026,40(2):137-141,156,6.基金项目
2025年度长沙民政职业技术学院教授、博士科研项目(2025JB18) （2025JB18）
