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DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training

arXiv 2409.07127 ↗First posted 11 Sep 2024cs.AI cs.MAcited by 0

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Abstract (arXiv)

Efficient communication can enhance the overall performance of collaborative multi-agent reinforcement learning. A common approach is to share observations through full communication, leading to significant communication overhead. Existing work attempts to perceive the global state by conducting teammate model based on local information. However, they ignore that the uncertainty generated by prediction may lead to difficult training. To address this problem, we propose a Demand-aware Customized Multi-Agent Communication (DCMAC) protocol, which use an upper bound training to obtain the ideal policy. By utilizing the demand parsing module, agent can interpret the gain of sending local message on teammate, and generate customized messages via compute the correlation between demands and local observation using cross-attention mechanism. Moreover, our method can adapt to the communication resources of agents and accelerate the training progress by appropriating the ideal policy which is trained with joint observation. Experimental results reveal that DCMAC significantly outperforms the baseline algorithms in both unconstrained and communication constrained scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 41 references that are in the map. First posted on arXiv 11 Sep 2024.

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References · 34

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  3. 2016Deep Variational Information Bottleneckcited by 930
  4. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  5. 2019The StarCraft Multi-Agent Challengecited by 597
  6. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  7. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  8. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  9. 2020QPLEX: Duplex Dueling Multi-Agent Q-Learningcited by 270
  10. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 261
  11. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 212
  12. 2018TarMAC: Targeted Multi-Agent Communicationcited by 208
22 more
  1. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  2. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 186
  3. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  4. 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 177
  5. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 175
  6. 2022Multi-Agent Reinforcement Learning is a Sequence Modeling Problemcited by 155
  7. 2018Graph Convolutional Reinforcement Learningcited by 152
  8. 2020ROMA: Multi-Agent Reinforcement Learning with Emergent Rolescited by 137
  9. 2018Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Taskscited by 121
  10. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 104
  11. 2019Learning to Schedule Communication in Multi-agent Reinforcement Learningcited by 93
  12. 2019Learning Nearly Decomposable Value Functions Via Communication Minimizationcited by 86
  13. 2019Multi-Agent Game Abstraction via Graph Attention Neural Networkcited by 82
  14. 2020Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learningcited by 60
  15. 2021ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mindcited by 58
  16. 2021Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learningcited by 56
  17. 2020Learning Individually Inferred Communication for Multi-Agent Cooperationcited by 55
  18. 2019Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Controlcited by 46
  19. 2020Succinct and Robust Multi-Agent Communication With Temporal Message Controlcited by 29
  20. 2023Context-aware Communication for Multi-agent Reinforcement Learningcited by 13
  21. 2023RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learningcited by 8
  22. 2023Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanismcited by 6

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