空天预警研究学报2026，Vol.40Issue(2)：142-147,6.DOI:10.3969/j.issn.2097-180X.2026.02.013
基于A2C-DDQN网络的异构任务分布式卸载优化算法
A distributed offloading optimization algorithm for heterogeneous tasks based on A2C-DDQN network
韦晶 1康希平2
作者信息
- 1. 长沙民政职业技术学院软件学院,长沙 410004
- 2. 长沙开元仪器有限公司,长沙 410100
- 折叠
摘要
Abstract
In mobile edge computing(MEC),heterogeneous tasks need to be processed as a whole due to their indivisibility,while the existing distributed offloading algorithms have problems such as insufficient adapta-tion to heterogeneous features and difficulty in collaborative optimization of delay and energy consumption.In re-sponse to the aforementioned issue,this paper proposes a heterogeneous task distributed offloading optimization(HDOO)algorithm.The HDOO algorithm integrates advantage actor-critic(A2C)and double deep Q-network(DDQN)technologies to construct an A2C-DDQN network so as to improve training efficiency and policy stabili-ty,and then introduces a heterogeneous task feature perception module to adapt to the requirements of tasks with different computational complexities.Simulation results show that,compared with typical deep reinforcement learning offloading algorithms,the HDOO algorithm has significant optimizations in terms of such indicators as average delay,energy consumption and task drop rate,effectively improving the overall performance of the MEC system.关键词
移动边缘计算/异构任务/分布式卸载/优势演员-评论家/双深度Q网络Key words
mobile edge computing/heterogeneous tasks/distributed offloading/A2C/DDQN分类
信息技术与安全科学引用本文复制引用
韦晶,康希平..基于A2C-DDQN网络的异构任务分布式卸载优化算法[J].空天预警研究学报,2026,40(2):142-147,6.
