mlsanityData to 7 Oct 2026

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

arXiv 1605.06676 ↗First posted 21 May 2016cs.AI cs.LG cs.MAcited by 561

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Cited by 53 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains.

Key facts. Cited by 561 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 May 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 111; 2022: 101; 2023: 99; 2024: 71; 2025: 78; 2026: 99.

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  1. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  2. 2023Habitat 3.0: A Co-Habitat for Humans, Avatars and Robotscited by 220
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  7. 2022SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learningcited by 118
  8. 2023Agents: An Open-source Framework for Autonomous Language Agentscited by 116
  9. 2024FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Makingcited by 112
  10. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  11. 2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
  12. 2021Celebrating Diversity in Shared Multi-Agent Reinforcement Learningcited by 96
38 more
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  5. 2024LLM-based Multi-Agent Reinforcement Learning: Current and Future Directionscited by 67
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  9. 2025Embodied AI Agents: Modeling the Worldcited by 57
  10. 2021Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learningcited by 54
  11. 2023Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perceptioncited by 52
  12. 2022Towards a Standardised Performance Evaluation Protocol for Cooperative MARLcited by 48
  13. 2024Learning Multi-Agent Communication from Graph Modeling Perspectivecited by 47
  14. 2024Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitatecited by 46
  15. 2024An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learningcited by 46
  16. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learningcited by 43
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  18. 2022Fortuitous Forgetting in Connectionist Networkscited by 39
  19. 2021Learning to Ground Multi-Agent Communication with Autoencoderscited by 37
  20. 2025Multi-Agent Coordination across Diverse Applications: A Surveycited by 37
  21. 2021On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learningcited by 35
  22. 2023Heterogeneous Multi-Robot Reinforcement Learningcited by 34
  23. 2021Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimizationcited by 33
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  25. 2022Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learningcited by 32
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  28. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 30
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  32. 2023A Review of Cooperation in Multi-agent Learningcited by 27
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