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

Kaiqing Zhang, Zhuoran Yang, Tamer Başar

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)802-814
Number of pages13
JournalFrontiers of Information Technology and Electronic Engineering
Issue number6
StatePublished - Jun 2021
Externally publishedYes


  • Consensus optimization
  • Distributed optimization
  • Game theory
  • Multi-agent systems
  • Networked systems
  • Reinforcement learning
  • TP18

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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