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Interference-Aware K-Step Reachable Communication in Multi-Agent Reinforcement Learning

arXiv 2603.15054 ↗First posted 16 Mar 2026cs.AIcited by 0

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Abstract (arXiv)

Effective communication is pivotal for addressing complex collaborative tasks in multi-agent reinforcement learning (MARL). Yet, limited communication bandwidth and dynamic, intricate environmental topologies present significant challenges in identifying high-value communication partners. Agents must consequently select collaborators under uncertainty, lacking a priori knowledge of which partners can deliver task-critical information. To this end, we propose Interference-Aware K-Step Reachable Communication (IA-KRC), a novel framework that enhances cooperation via two core components: (1) a K-Step reachability protocol that confines message passing to physically accessible neighbors, and (2) an interference-prediction module that optimizes partner choice by minimizing interference while maximizing utility. Compared to existing methods, IA-KRC enables substantially more persistent and efficient cooperation despite environmental interference. Comprehensive evaluations confirm that IA-KRC achieves superior performance compared to state-of-the-art baselines, while demonstrating enhanced robustness and scalability in complex topological and highly dynamic multi-agent scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 16 Mar 2026.

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25 of 42 references.

References · 25

  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. 2019The StarCraft Multi-Agent Challengecited by 597
  4. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  5. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
  6. 2017FeUdal Networks for Hierarchical Reinforcement Learningcited by 337
  7. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 313
  8. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 175
  9. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  10. 2018Graph Convolutional Reinforcement Learningcited by 152
  11. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 104
  12. 2021Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharingcited by 87
13 more
  1. 2020Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learningcited by 58
  2. 2024Learning Multi-Agent Communication from Graph Modeling Perspectivecited by 48
  3. 2018Deep Reinforcement Learning for Swarm Systemscited by 45
  4. 2020Multi-Agent Determinantal Q-Learningcited by 36
  5. 2020Learning Structured Communication for Multi-agent Reinforcement Learningcited by 21
  6. 2020Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learningcited by 14
  7. 2022Multi-Agent Coordination via Multi-Level Communicationcited by 14
  8. 2021Neurosymbolic Transformers for Multi-Agent Communicationcited by 13
  9. 2020Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communicationcited by 12
  10. 2023Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systemscited by 6
  11. 2024Bridging Training and Execution via Dynamic Directed Graph-Based Communication in Cooperative Multi-Agent Systemscited by 5
  12. 2021The Power of Communication in a Distributed Multi-Agent Systemcited by 4
  13. 2020Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoringcited by 3

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