mlsanityData to 7 Oct 2026

Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

arXiv 1812.09755 ↗First posted 23 Dec 2018cs.LG cs.AI cs.MAcited by 114

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Abstract (arXiv)

Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

Key facts. Cited by 114 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Dec 2018.

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Citing papers per year: 2021: 18; 2022: 26; 2023: 26; 2024: 10; 2025: 13; 2026: 21.

114 papers.

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  1. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  2. 2022Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Mapscited by 255
  3. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  4. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  5. 2021Celebrating Diversity in Shared Multi-Agent Reinforcement Learningcited by 96
  6. 2023Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perceptioncited by 52
  7. 2022Towards a Standardised Performance Evaluation Protocol for Cooperative MARLcited by 48
  8. 2024Learning Multi-Agent Communication from Graph Modeling Perspectivecited by 47
  9. 2025Multi-Agent Coordination across Diverse Applications: A Surveycited by 37
  10. 2021Learning Selective Communication for Multi-Agent Path Findingcited by 32
  11. 2025Internet of Agents: Fundamentals, Applications, and Challengescited by 31
  12. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 30
38 more
  1. 2025LLM-Based Human-Agent Collaboration and Interaction Systems: A Surveycited by 28
  2. 2023A Review of Cooperation in Multi-agent Learningcited by 27
  3. 2022Scalable Multi-Agent Model-Based Reinforcement Learningcited by 24
  4. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  5. 2021Collaborative Visual Navigationcited by 18
  6. 2021Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learningcited by 18
  7. 2021Emergent Communication under Competitioncited by 17
  8. 2024Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communicationcited by 17
  9. 2022An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobilitycited by 16
  10. 2021Self-Organized Polynomial-Time Coordination Graphscited by 14
  11. 2021Neurosymbolic Transformers for Multi-Agent Communicationcited by 13
  12. 2021Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learningcited by 11
  13. 2022Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantizationcited by 11
  14. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 11
  15. 2022Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RLcited by 11
  16. 2023From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARLcited by 11
  17. 2023Referential communication in heterogeneous communities of pre-trained visual deep networkscited by 10
  18. 2023Learning Multi-Agent Communication with Contrastive Learningcited by 9
  19. 2025mmCooper: A Multi-agent Multi-stage Communication-efficient and Collaboration-robust Cooperative Perception Frameworkcited by 9
  20. 2024T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integrationcited by 8
  21. 2022FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systemscited by 7
  22. 2023RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learningcited by 7
  23. 2023Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Controlcited by 7
  24. 2025Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learningcited by 7
  25. 2022Interpretable Learned Emergent Communication for Human-Agent Teamscited by 6
  26. 2022Scalable Model-based Policy Optimization for Decentralized Networked Systemscited by 6
  27. 2022CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spacescited by 6
  28. 2023Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanismcited by 6
  29. 2022Communication-Efficient Actor-Critic Methods for Homogeneous Markov Gamescited by 5
  30. 2022Learning Latent Representations to Co-Adapt to Humanscited by 5
  31. 2023Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspectivecited by 5
  32. 2023Learning Models of Adversarial Agent Behavior under Partial Observabilitycited by 5
  33. 2024The Bandit Whisperer: Communication Learning for Restless Banditscited by 5
  34. 2026TodyComm: Task-Oriented Dynamic Communication for Multi-Round LLM-based Multi-Agent Systemcited by 5
  35. 2022Learning to Ground Decentralized Multi-Agent Communication with Contrastive Learningcited by 4
  36. 2022Collaborative 3D Object Detection for Automatic Vehicle Systems via Learnable Communicationscited by 4
  37. 2022Over-communicate no more: Situated RL agents learn concise communication protocolscited by 4
  38. 2023Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learningcited by 4

50 most cited of 114 All 114 (JSON)