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Multi-agent Reinforcement Learning for Networked System Control

arXiv 2004.01339 ↗First posted 3 Apr 2020cs.LG stat.MLcited by 51

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Abstract (arXiv)

This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent. Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL. Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Apr 2020.

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Citing papers per year: 2021: 12; 2022: 6; 2023: 12; 2024: 8; 2025: 8; 2026: 5.

51 papers.

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  1. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  2. 2025Multi-agent Embodied AI: Advances and Future Directionscited by 36
  3. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 30
  4. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  5. 2023A Review of Cooperation in Multi-agent Learningcited by 27
  6. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Trafficcited by 20
  7. 2022An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobilitycited by 16
  8. 2024Generative Emergent Communication: Large Language Model is a Collective World Modelcited by 15
  9. 2026The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMscited by 10
  10. 2023A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Managementcited by 9
  11. 2024Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scalecited by 8
  12. 2025SRMT: Shared Memory for Multi-agent Lifelong Pathfindingcited by 8
38 more
  1. 2021Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learningcited by 7
  2. 2021OnSlicing: Online End-to-End Network Slicing with Reinforcement Learningcited by 7
  3. 2023Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Controlcited by 7
  4. 2025Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learningcited by 7
  5. 2021MDPGT: Momentum-based Decentralized Policy Gradient Trackingcited by 6
  6. 2022Scalable Model-based Policy Optimization for Decentralized Networked Systemscited by 6
  7. 2021Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problemcited by 5
  8. 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Controlcited by 5
  9. 2024Provable Policy Gradient Methods for Average-Reward Markov Potential Gamescited by 5
  10. 2021Learning to Share in Multi-Agent Reinforcement Learningcited by 4
  11. 2021Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systemscited by 3
  12. 2024Fully Independent Communication in Multi-Agent Reinforcement Learningcited by 3
  13. 2024Approximate Global Convergence of Independent Learning in Multi-Agent Systemscited by 3
  14. 2022Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systemscited by 2
  15. 2023Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Propertycited by 2
  16. 2024Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Controlcited by 2
  17. 2025Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learningcited by 2
  18. 2021Modelling resource allocation in uncertain system environment through deep reinforcement learningcited by 1
  19. 2021Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Studycited by 1
  20. 2021Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Modulecited by 1
  21. 2023Kindness in Multi-Agent Reinforcement Learningcited by 1
  22. 2025Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Controlcited by 1
  23. 2025Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learningcited by 1
  24. 2026CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Controlcited by 1
  25. 2026Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamicscited by 1
  26. 2021Towards Intelligent RAN Slicing for B5G: Opportunities and Challengescited by 0
  27. 2021Effects of Smart Traffic Signal Control on Air Qualitycited by 0
  28. 2022Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximationcited by 0
  29. 2023Low Entropy Communication in Multi-Agent Reinforcement Learningcited by 0
  30. 2023Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networkscited by 0
  31. 2023Characterizing Speed Performance of Multi-Agent Reinforcement Learningcited by 0
  32. 2023Staged Reinforcement Learning for Complex Tasks through Decomposed Environmentscited by 0
  33. 2023Environmental-Impact Based Multi-Agent Reinforcement Learningcited by 0
  34. 2023Sparse Mean Field Load Balancing in Large Localized Queueing Systemscited by 0
  35. 2024Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharingcited by 0
  36. 2025LOD: Latent Objective Discovery in Heterogeneous Multi-Agent Reinforcement Learningcited by 0
  37. 2025Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learningcited by 0
  38. 2026Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learningcited by 0

50 most cited of 51 All 51 (JSON)