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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2100331


Multi-agent deep reinforcement learning for end-edge orchestrated resource allocation in industrial wireless networks


Author(s):  Xiaoyu LIU, Chi XU, Haibin YU, Peng ZENG

Affiliation(s):  State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; more

Corresponding email(s):   liuxiaoyu1@sia.cn, xuchi@sia.cn, yhb@sia.cn, zp@sia.cn

Key Words:  Multi-agent deep reinforcement learning, End-edge orchestrated, Industrial wireless networks, Delay, Energy consumption


Xiaoyu LIU, Chi XU, Haibin YU, Peng ZENG. Multi-agent deep reinforcement learning for end-edge orchestrated resource allocation in industrial wireless networks[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
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 learningbased 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-step ε-greedy method to balance exploitation and exploration. Finally, we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in numerous experiments, which show that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.

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