典型文献
Multi-agent reinforcement learning for edge information sharing in vehicular networks
文献摘要：
To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay re-quirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two prob-lems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.
文献关键词：
中图分类号：
作者姓名：
Ruyan Wang;Xue Jiang;Yujie Zhou;Zhidu Li;Dapeng Wu;Tong Tang;Alexander Fedotov;Vladimir Badenko
作者机构：
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications,Chongqing,400065,China;Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China,Chongqing,400065,China;Key Laboratory of Ubiquitous Sensing and Networking in Chongqing,Chongqing,400065,China;Peter the Great St.Petersburg Polytechnic University,Polytechnicheskaya,29,St.Petersburg,195251,Russia
文献出处：
引用格式：
[1]Ruyan Wang;Xue Jiang;Yujie Zhou;Zhidu Li;Dapeng Wu;Tong Tang;Alexander Fedotov;Vladimir Badenko-.Multi-agent reinforcement learning for edge information sharing in vehicular networks)[J].数字通信与网络（英文）,2022(03):267-277
A类：
B类：
Multi,agent,reinforcement,learning,edge,information,sharing,vehicular,networks,To,guarantee,heterogeneous,delay,requirements,diverse,services,necessary,full,cooperative,policy,both,Vehicle,Infrastructure,V2I,V2V,links,This,paper,investigates,reduction,while,satisfying,Specifically,mean,minimization,problem,maximum,individual,formulated,improve,global,performance,ensure,fairness,single,user,respectively,multi,framework,designed,solve,these,lems,where,new,reward,function,proposed,evaluate,utilities,optimization,objectives,unified,Thereafter,proximal,enable,each,its,using,shared,effectiveness,finally,validated,by,comparing,obtained,results,those,other,baseline,approaches,through,extensive,simulation,experiments
AB值：
0.564443
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