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2021, Volume 22, Issue 6

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Frontiers of Information Technology & Electronic Engineering >> 2021, Volume 22, Issue 6 doi: 10.1631/FITEE.1900661

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

Affiliation(s): Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, IL 61801, USA; Department of Operations Research and Financial Engineering, Princeton University, NJ 08544, USA; less

Received: 2019-11-30 Accepted: 2021-07-12 Available online: 2021-07-12
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Abstract

Multi-agent (MARL) has long been a significant research topic in both machine learning and control systems. Recent development of (single-agent) deep 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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