CLC number:
On-line Access: 2022-01-24
Received: 2021-07-05
Revision Accepted: 2022-04-22
Crosschecked: 2021-11-15
Cited: 0
Clicked: 4739
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0003-3293-0803
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, 2022, 23(1): 47-60.
@article{title="Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wireless networks",
author="Xiaoyu LIU, Chi XU, Haibin YU, Peng ZENG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="1",
pages="47-60",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100331"
}
%0 Journal Article
%T Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wireless networks
%A Xiaoyu LIU
%A Chi XU
%A Haibin YU
%A Peng ZENG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 1
%P 47-60
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100331
TY - JOUR
T1 - Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wireless networks
A1 - Xiaoyu LIU
A1 - Chi XU
A1 - Haibin YU
A1 - Peng ZENG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 1
SP - 47
EP - 60
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100331
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 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-step
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