CLC number: TP18
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-04-29
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Kaiqing Zhang, Zhuoran Yang, Tamer Başar. Decentralized multi-agent reinforcement learning with networked agents: recent advances[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(6): 802-814.
@article{title="Decentralized multi-agent reinforcement learning with networked agents: recent advances",
author="Kaiqing Zhang, Zhuoran Yang, Tamer Başar",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="6",
pages="802-814",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900661"
}
%0 Journal Article
%T Decentralized multi-agent reinforcement learning with networked agents: recent advances
%A Kaiqing Zhang
%A Zhuoran Yang
%A Tamer Başar
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 6
%P 802-814
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900661
TY - JOUR
T1 - Decentralized multi-agent reinforcement learning with networked agents: recent advances
A1 - Kaiqing Zhang
A1 - Zhuoran Yang
A1 - Tamer Başar
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 6
SP - 802
EP - 814
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900661
Abstract: Multi-agent reinforcement learning (MARL) has long been a significant research topic in both machine learning and control systems. Recent development of (single-agent) deep reinforcement learning has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents. In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. We hope that this review promotes additional research efforts in this exciting yet challenging area.
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