文献类型：期刊文献

英文题名：Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

作者：Lu, Haifeng[1];Gu, Chunhua[1];Luo, Fei[1];Ding, Weichao[1];Zheng, Shuai[1];Shen, Yifan[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：8

起止页码：202573

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20211210120994);WOS:【SCI-EXPANDED(收录号:WOS:000590424400001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61472139, in part by the Shanghai Automobile Industry Science and Technology Development Foundation under Grant H100-2-19160, and in part by the Shanghai Sailing Program under Grant 20YF1410900.

语种：英文

外文关键词：Mobile edge computing; task offloading; wireless power transfer; multi-agent; deep reinforcement learning

摘要：Combined with wireless power transfer (WPT) technology, mobile edge computing can provide continuous energy supply and computing resources for mobile devices, and improve their battery life and business application scenarios. This article first designs the mobile edge computing (MEC) model of mobile devices with random mobility and hybrid access point (HAP) with data transmission and energy transmission. On this basis, the selection of target server and the amount of data offloading are taken as the learning objectives, and the task offloading strategy based on multi-agent deep reinforcement learning is constructed. Then combined with MADDPG algorithm and SAC algorithm, the problems of multi-agent environment instability and the difficulty of convergence are solved. The final experimental results show that the improved algorithm based on MADDPG and SAC has good stability and convergence. Compared with other algorithms, it has achieved good results in energy consumption, delay and task failure rate.