mlsanityData to 7 Oct 2026

Learning Multiagent Communication with Backpropagation

arXiv 1605.07736 ↗First posted 25 May 2016cs.LG cs.AIcited by 355

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Cited by 28 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

Key facts. Cited by 355 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 May 2016.

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Citing papers per year: 2020: 1; 2021: 62; 2022: 65; 2023: 65; 2024: 38; 2025: 48; 2026: 76.

355 papers.

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  1. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  2. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
  3. 2022Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Mapscited by 255
  4. 2023Habitat 3.0: A Co-Habitat for Humans, Avatars and Robotscited by 220
  5. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  6. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  7. 2021From Motor Control to Team Play in Simulated Humanoid Footballcited by 87
  8. 2023Collaborative Perception in Autonomous Driving: Methods, Datasets and Challengescited by 87
  9. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  10. 2021Residual Pathway Priors for Soft Equivariance Constraintscited by 74
  11. 2024LLM-based Multi-Agent Reinforcement Learning: Current and Future Directionscited by 67
  12. 2021ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mindcited by 57
38 more
  1. 2023Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perceptioncited by 52
  2. 2025MARFT: Multi-Agent Reinforcement Fine-Tuningcited by 51
  3. 2022Towards a Standardised Performance Evaluation Protocol for Cooperative MARLcited by 48
  4. 2024Learning Multi-Agent Communication from Graph Modeling Perspectivecited by 47
  5. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learningcited by 43
  6. 2021Spatial Dual-Modality Graph Reasoning for Key Information Extractioncited by 43
  7. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  8. 2021Learning to Ground Multi-Agent Communication with Autoencoderscited by 37
  9. 2025Multi-Agent Coordination across Diverse Applications: A Surveycited by 37
  10. 2022Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregationcited by 34
  11. 2023Heterogeneous Multi-Robot Reinforcement Learningcited by 34
  12. 2025Internet of Agents: Fundamentals, Applications, and Challengescited by 31
  13. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 30
  14. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  15. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  16. 2021Conditional Neural Relational Inference for Interacting Systemscited by 28
  17. 2024LLM-Empowered State Representation for Reinforcement Learningcited by 28
  18. 2023A Review of Cooperation in Multi-agent Learningcited by 27
  19. 2021SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learningcited by 26
  20. 2021Interpretable agent communication from scratch (with a generic visual processor emerging on the side)cited by 25
  21. 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Controlcited by 25
  22. 2021Multi-Agent MDP Homomorphic Networkscited by 24
  23. 2022Scalable Multi-Agent Model-Based Reinforcement Learningcited by 24
  24. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 23
  25. 2021Iterated learning for emergent systematicity in VQAcited by 21
  26. 2022What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?cited by 21
  27. 2021Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learningcited by 20
  28. 2021Emergent Discrete Communication in Semantic Spacescited by 20
  29. 2021Learning Efficient Multi-Agent Cooperative Visual Explorationcited by 19
  30. 2021Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agentscited by 19
  31. 2021HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanismcited by 19
  32. 2023RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorizationcited by 19
  33. 2021Collaborative Visual Navigationcited by 18
  34. 2021Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learningcited by 18
  35. 2022Reinforcement learning on graphs: A surveycited by 18
  36. 2022Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learningcited by 18
  37. 2023Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?cited by 18
  38. 2023Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Explorationcited by 17

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