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Succinct and Robust Multi-Agent Communication With Temporal Message Control

arXiv 2010.14391 ↗First posted 27 Oct 2020cs.AI cs.LG cs.MAcited by 29

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Abstract (arXiv)

Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). However, existing communication schemes often require agents to exchange an excessive number of messages at run-time under a reliable communication channel, which hinders its practicality in many real-world situations. In this paper, we present textitTemporal Message Control (TMC), a simple yet effective approach for achieving succinct and robust communication in MARL. TMC applies a temporal smoothing technique to drastically reduce the amount of information exchanged between agents. Experiments show that TMC can significantly reduce inter-agent communication overhead without impacting accuracy. Furthermore, TMC demonstrates much better robustness against transmission loss than existing approaches in lossy networking environments.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2020.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 9; 2024: 3; 2025: 2; 2026: 4.

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  1. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  2. 2021Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharingcited by 85
  3. 2021Cooperative Exploration for Multi-Agent Deep Reinforcement Learningcited by 57
  4. 2022Towards a Standardised Performance Evaluation Protocol for Cooperative MARLcited by 48
  5. 2021Learning Selective Communication for Multi-Agent Path Findingcited by 32
  6. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 28
  7. 2021Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learningcited by 18
  8. 2023Context-aware Communication for Multi-agent Reinforcement Learningcited by 13
  9. 2024DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptionscited by 13
  10. 2022DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learningcited by 9
  11. 2024T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integrationcited by 8
  12. 2025Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learningcited by 7
17 more
  1. 2022Trust-based Consensus in Multi-Agent Reinforcement Learning Systemscited by 6
  2. 2023Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanismcited by 6
  3. 2023Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generationcited by 6
  4. 2023Robust Communicative Multi-Agent Reinforcement Learning with Active Defensecited by 4
  5. 2023Robust Multi-agent Communication via Multi-view Message Certificationcited by 3
  6. 2025Multi-Agent Reinforcement Learning with Communication-Constrained Priorscited by 3
  7. 2022Universally Expressive Communication in Multi-Agent Reinforcement Learningcited by 2
  8. 2022Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learningcited by 2
  9. 2023Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learningcited by 2
  10. 2026LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learningcited by 2
  11. 2023Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidthcited by 1
  12. 2023DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learningcited by 1
  13. 2023Low Entropy Communication in Multi-Agent Reinforcement Learningcited by 0
  14. 2024DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Trainingcited by 0
  15. 2026Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learningcited by 0
  16. 2026Finding the Weakest Link: Adversarial Attack against Multi-Agent Communicationscited by 0
  17. 2026HiComm: Hierarchical Communication for Multi-agent Reinforcement Learningcited by 0