测试科学与仪器2021，Vol.12Issue(4)：489-500,12.DOI:10.3969/j.issn.1674-8042.2021.04.013
基于深度强化学习多用户移动边缘计算轻量任务卸载优化
Deep reinforcement learning-based optimization of lightweight task offloading for multi-user mobile edge computing
摘要
Abstract
To improve the quality of computation experience for mobile devices,mobile edge computing (MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which supports both traditional communication and MEC services.However,this kind of intensive computing problem is a high dimensional NP hard problem,and some machine learning methods do not have a good effect on solving this problem.In this paper,the Markov decision process model is established to find the excellent task offloading scheme,which maximizes the long-term utility performance,so as to make the best offloading decision according to the queue state,energy queue state and channel quality between mobile users and BS.In order to explore the curse of high dimension in state space,a candidate network is proposed based on edge computing optimize offloading (ECOO)algorithm with the application of deep deterministic policy gradient algorithm.Through simulation experiments,it is proved that the ECOO algorithm is superior to some deep reinforcement learning algorithms in terms of energy consumption and time delay.So the ECOO is good at dealing with high dimensional problems.关键词
多用户移动边缘计算/任务卸载/深度强化学习Key words
multi-user mobile edge computing/task offloading/deep reinforcement learning引用本文复制引用
张文献,杜永文..基于深度强化学习多用户移动边缘计算轻量任务卸载优化[J].测试科学与仪器,2021,12(4):489-500,12.基金项目
National Natural Science Foundation of China(No.11461038) （No.11461038）
Science and Technology Support Program of Gansu Province(No.144NKCA040) （No.144NKCA040）
