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Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

arXiv 1909.02682 ↗First posted 6 Sep 2019cs.LG stat.MLcited by 46

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Abstract (arXiv)

Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving efficient communication among agents has always been an overarching problem in MARL. In this work, we propose Variance Based Control (VBC), a simple yet efficient technique to improve communication efficiency in MARL. By limiting the variance of the exchanged messages between agents during the training phase, the noisy component in the messages can be eliminated effectively, while the useful part can be preserved and utilized by the agents for better performance. Our evaluation using a challenging set of StarCraft II benchmarks indicates that our method achieves 2-10× lower in communication overhead than state-of-the-art MARL algorithms, while allowing agents to better collaborate by developing sophisticated strategies.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2019.

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Citing papers per year: 2021: 4; 2022: 14; 2023: 12; 2024: 4; 2025: 5; 2026: 7.

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  1. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  2. 2024Embodied LLM Agents Learn to Cooperate in Organized Teamscited by 81
  3. 2022Towards a Standardised Performance Evaluation Protocol for Cooperative MARLcited by 48
  4. 2021Learning Selective Communication for Multi-Agent Path Findingcited by 32
  5. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 28
  6. 2021DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learningcited by 26
  7. 2022PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaborationcited by 23
  8. 2022Multi-Agent Coordination via Multi-Level Communicationcited by 14
  9. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
  10. 2023Context-aware Communication for Multi-agent Reinforcement Learningcited by 13
  11. 2021Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictabilitycited by 11
  12. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 11
34 more
  1. 2023From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARLcited by 11
  2. 2024T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integrationcited by 8
  3. 2025SRMT: Shared Memory for Multi-agent Lifelong Pathfindingcited by 8
  4. 2021Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learningcited by 7
  5. 2023Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Controlcited by 7
  6. 2023Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanismcited by 6
  7. 2023Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generationcited by 6
  8. 2022Communication-Efficient Actor-Critic Methods for Homogeneous Markov Gamescited by 5
  9. 2023AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learningcited by 5
  10. 2022Over-communicate no more: Situated RL agents learn concise communication protocolscited by 4
  11. 2022Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestationcited by 4
  12. 2023Robust Multi-agent Communication via Multi-view Message Certificationcited by 3
  13. 2024Fully Independent Communication in Multi-Agent Reinforcement Learningcited by 3
  14. 2025Multi-Agent Reinforcement Learning with Communication-Constrained Priorscited by 3
  15. 2026GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systemscited by 3
  16. 2022Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Gamecited by 2
  17. 2022Universally Expressive Communication in Multi-Agent Reinforcement Learningcited by 2
  18. 2022Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learningcited by 2
  19. 2023Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learningcited by 2
  20. 2025Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordinationcited by 2
  21. 2022Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learningcited by 1
  22. 2023A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learningcited by 1
  23. 2023Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidthcited by 1
  24. 2023DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learningcited by 1
  25. 2022Graph Convolutional Reinforcement Learning for Collaborative Queuing Agentscited by 0
  26. 2023Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learningcited by 0
  27. 2024DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Trainingcited by 0
  28. 2025Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teamscited by 0
  29. 2026Cooperative Multi-agent RL with Communication Constraintscited by 0
  30. 2026Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learningcited by 0
  31. 2026Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learningcited by 0
  32. 2026Decoupling Communication from Policy: Robust MARL under Bandwidth Constraintscited by 0
  33. 2026HiComm: Hierarchical Communication for Multi-agent Reinforcement Learningcited by 0
  34. 2026Robust and Efficient Communication for Multi-Agent Learningcited by 0