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Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances

arXiv 1912.03821 ↗First posted 9 Dec 2019cs.LG cs.AI cs.MAcited by 23

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Abstract (arXiv)

Multi-agent reinforcement learning (MARL) has long been a significant and everlasting research topic in both machine learning and control. With the recent development of (single-agent) deep RL, there is a resurgence of interests in developing new MARL algorithms, especially those that are backed by theoretical analysis. In this paper, we review some recent advances a sub-area of this topic: decentralized MARL with networked agents. Specifically, multiple agents perform sequential decision-making in a common environment, without the coordination of any central controller. Instead, the agents are 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 smart grid. This review is built upon several our research endeavors in this direction, together with some progresses made by other researchers along the line. We hope this review to inspire the devotion of more research efforts to this exciting yet challenging area.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Dec 2019.

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Citing papers per year: 2021: 10; 2022: 3; 2023: 5; 2024: 3; 2025: 0; 2026: 2.

23 papers.

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  1. 2023A Review of Cooperation in Multi-agent Learningcited by 27
  2. 2021MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learningcited by 23
  3. 2021Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structurecited by 14
  4. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 11
  5. 2021Common Information based Approximate State Representations in Multi-Agent Reinforcement Learningcited by 8
  6. 2021MDPGT: Momentum-based Decentralized Policy Gradient Trackingcited by 6
  7. 2023Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behaviorcited by 6
  8. 2021Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-levelcited by 5
  9. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  10. 2021Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspectivecited by 4
  11. 2021Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibrationcited by 4
  12. 2022Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agentscited by 3
11 more
  1. 2022Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behaviorcited by 3
  2. 2021Scalable regret for learning to control network-coupled subsystems with unknown dynamicscited by 2
  3. 2021Multi-agent Natural Actor-critic Reinforcement Learning Algorithmscited by 2
  4. 2024Scalable Interactive Machine Learning for Future Command and Controlcited by 2
  5. 2023Decentralised Q-Learning for Multi-Agent Markov Decision Processes with a Satisfiability Criterioncited by 1
  6. 2021Distributed Algorithms for Linearly-Solvable Optimal Control in Networked Multi-Agent Systemscited by 0
  7. 2023Networked Communication for Decentralised Agents in Mean-Field Gamescited by 0
  8. 2024Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learningcited by 0
  9. 2024Single-Loop Federated Actor-Critic across Heterogeneous Environmentscited by 0
  10. 2026Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problemscited by 0
  11. 2026PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillationcited by 0