mlsanityData to 7 Oct 2026

Learning Attentional Communication for Multi-Agent Cooperation

arXiv 1805.07733 ↗First posted 20 May 2018cs.AI cs.MAcited by 170

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Abstract (arXiv)

Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that existing methods adopt can be problematic. When there is a large number of agents, agents cannot differentiate valuable information that helps cooperative decision making from globally shared information. Therefore, communication barely helps, and could even impair the learning of multi-agent cooperation. Predefined communication architectures, on the other hand, restrict communication among agents and thus restrain potential cooperation. To tackle these difficulties, in this paper, we propose an attentional communication model that learns when communication is needed and how to integrate shared information for cooperative decision making. Our model leads to efficient and effective communication for large-scale multi-agent cooperation. Empirically, we show the strength of our model in a variety of cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies than existing methods.

Key facts. Cited by 170 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2018.

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Citing papers per year: 2021: 27; 2022: 43; 2023: 34; 2024: 23; 2025: 21; 2026: 22.

170 papers.

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  1. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  2. 2023Building Cooperative Embodied Agents Modularly with Large Language Modelscited by 231
  3. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  4. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
  5. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  6. 2023Collaborative Perception in Autonomous Driving: Methods, Datasets and Challengescited by 87
  7. 2024LLM-based Multi-Agent Reinforcement Learning: Current and Future Directionscited by 67
  8. 2021Cooperative Exploration for Multi-Agent Deep Reinforcement Learningcited by 57
  9. 2021Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learningcited by 54
  10. 2023Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perceptioncited by 52
  11. 2022Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulationcited by 49
  12. 2024Learning Multi-Agent Communication from Graph Modeling Perspectivecited by 47
38 more
  1. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  2. 2022Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregationcited by 34
  3. 2025Internet of Agents: Fundamentals, Applications, and Challengescited by 31
  4. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 30
  5. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  6. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 28
  7. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 27
  8. 2023A Review of Cooperation in Multi-agent Learningcited by 27
  9. 2021Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problemscited by 25
  10. 2022Scalable Multi-Agent Model-Based Reinforcement Learningcited by 24
  11. 2021Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learningcited by 20
  12. 2021GridToPix: Training Embodied Agents with Minimal Supervisioncited by 20
  13. 2021Learning Efficient Multi-Agent Cooperative Visual Explorationcited by 19
  14. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  15. 2021Collaborative Visual Navigationcited by 18
  16. 2021Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learningcited by 18
  17. 2023Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?cited by 18
  18. 2023Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Explorationcited by 17
  19. 2022An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobilitycited by 16
  20. 2022Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Modelscited by 16
  21. 2024Generative Emergent Communication: Large Language Model is a Collective World Modelcited by 15
  22. 2022Multi-Agent Policy Transfer via Task Relationship Modelingcited by 14
  23. 2022Multi-Agent Coordination via Multi-Level Communicationcited by 14
  24. 2025Cost-Effective Communication: An Auction-based Method for Language Agent Interactioncited by 14
  25. 2023Context-aware Communication for Multi-agent Reinforcement Learningcited by 13
  26. 2022Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradientscited by 12
  27. 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 12
  28. 2024CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperationcited by 12
  29. 2021Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learningcited by 11
  30. 2022Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantizationcited by 11
  31. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 11
  32. 2022Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RLcited by 11
  33. 2023From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARLcited by 11
  34. 2021Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid Worldcited by 10
  35. 2022Collaborative Perception for Autonomous Driving: Current Status and Future Trendcited by 10
  36. 2022Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learningcited by 10
  37. 2022Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learningcited by 9
  38. 2022DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learningcited by 9

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