Research Area: Machine Learning
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.
Keywords:
Reinforcement learning
Multi-agent systems
Networked systems
Consensus optimization
Distributed optimization
Game theory
Deep Learning
Author(s) Name: Kaiqing Zhang , Zhuoran Yang & Tamer Başar
Journal name: Frontiers of Information Technology & Electronic Engineering
Conferrence name:
Publisher name: Springer
DOI: 10.1631/FITEE.1900661
Volume Information: volume 22, pages: 802–814 (2021)
