典型文献
Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wireless networks
文献摘要：
Edge artificial intelligence will empower the ever simple industrial wireless networks (IWNs) supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices (MTDs) and edge servers. In this paper, we propose a multi-agent deep reinforcement learning based resource allocation (MADRL-RA) algorithm for end–edge orchestrated IWNs to support computation-intensive and delay-sensitive applications. First, we present the system model of IWNs, wherein each MTD is regarded as a self-learning agent. Then, we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy consumption. Next, we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision, computation capacity, and transmission power. To break the time correlation of training data while accelerating the learning process of MADRL-RA, we design a weighted experience replay to store and sample experiences categorically. Furthermore, we propose a step-by-stepε-greedy method to balance exploitation and exploration. Finally, we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments, showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.
文献关键词：
中图分类号：
作者姓名：
Xiaoyu LIU;Chi XU;Haibin YU;Peng ZENG
作者机构：
State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Key Laboratory of Networked Control Systems,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China;University of Chinese Academy of Sciences,Beijing 100049,China
文献出处：
引用格式：
[1]Xiaoyu LIU;Chi XU;Haibin YU;Peng ZENG-.Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wireless networks)[J].信息与电子工程前沿（英文）,2022(01):47-60
A类：
IWNs,categorically
B类：
Multi,agent,deep,reinforcement,learning,end,edge,orchestrated,allocation,industrial,wireless,networks,Edge,artificial,intelligence,will,empower,ever,simple,supporting,complex,dynamic,tasks,by,collaboratively,exploiting,computation,communication,resources,both,machine,type,devices,MTDs,servers,In,this,paper,propose,multi,MADRL,RA,intensive,delay,sensitive,applications,First,present,system,model,wherein,each,regarded,self,Then,apply,Markov,decision,process,formulate,minimum,overhead,problem,joint,optimization,energy,consumption,Next,employ,defeat,explosive,state,space,policy,respect,computing,capacity,transmission,To,break,correlation,training,data,while,accelerating,design,weighted,replay,store,sample,experiences,Furthermore,step,greedy,method,balance,exploitation,exploration,Finally,verify,effectiveness,comparing,some,benchmark,algorithms,many,experiments,showing,that,converges,quickly,learns,achieving
AB值：
0.569133
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