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

http://doi.org/10.1631/FITEE.1900661


Decentralized multi-agent reinforcement learning with networked agents: recent advances


Author(s):  Kai-qing ZHANG, Zhuo-ran YANG, Tamer BAŞ,AR

Affiliation(s):  Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, IL, USA; more

Corresponding email(s):   kzhang66@illinois.edu, zy6@princeton.edu, basar1@illinois.edu

Key Words:  Reinforcement learning, Multi-agent/networked systems, Consensus/distributed optimization, Game theory


Kai-qing ZHANG, Zhuo-ran YANG, Tamer BAŞAR. Decentralized multi-agent reinforcement learning with networked agents: recent advances[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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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 RL 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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