mlsanityData to 7 Oct 2026

DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning

arXiv 2212.01619 ↗First posted 3 Dec 2022cs.MA cs.LG cs.NIcited by 9

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Abstract (arXiv)

Communication is supposed to improve multi-agent collaboration and overall performance in cooperative Multi-agent reinforcement learning (MARL). However, such improvements are prevalently limited in practice since most existing communication schemes ignore communication overheads (e.g., communication delays). In this paper, we demonstrate that ignoring communication delays has detrimental effects on collaborations, especially in delay-sensitive tasks such as autonomous driving. To mitigate this impact, we design a delay-aware multi-agent communication model (DACOM) to adapt communication to delays. Specifically, DACOM introduces a component, TimeNet, that is responsible for adjusting the waiting time of an agent to receive messages from other agents such that the uncertainty associated with delay can be addressed. Our experiments reveal that DACOM has a non-negligible performance improvement over other mechanisms by making a better trade-off between the benefits of communication and the costs of waiting for messages.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 3 Dec 2022.

Builds on

17 of 29 references.

References · 17

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  3. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  4. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  5. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  6. 2016Learning Multiagent Communication with Backpropagationcited by 355
  7. 2013The Complexity of Decentralized Control of Markov Decision Processescited by 225
  8. 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 173
  9. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 170
  10. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  11. 2019Learning to Schedule Communication in Multi-agent Reinforcement Learningcited by 89
  12. 2020Delay-Aware Model-Based Reinforcement Learning for Continuous Controlcited by 33
5 more
  1. 2019Learning Agent Communication under Limited Bandwidth by Message Pruningcited by 31
  2. 2020Succinct and Robust Multi-Agent Communication With Temporal Message Controlcited by 29
  3. 2020Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environmentscited by 21
  4. 2020Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Gridscited by 10
  5. 2022PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communicationscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 2.

9 papers.

  1. 2025Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Modelscited by 11
  2. 2025Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learningcited by 7
  3. 2023AccDecoder: Accelerated Decoding for Neural-enhanced Video Analyticscited by 5
  4. 2025Multi-Agent Reinforcement Learning with Communication-Constrained Priorscited by 3
  5. 2026Reinforcement Learning for Control Systems with Time Delays: A Comprehensive Surveycited by 1
  6. 2024Prospective Messaging: Learning in Networks with Communication Delayscited by 0
  7. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  8. 2025Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observationcited by 0
  9. 2026Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learningcited by 0