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Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning

arXiv 2308.16198 ↗First posted 25 Aug 2023cs.LG cs.AI cs.MAcited by 0

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Abstract (arXiv)

Efficient information dissemination is crucial for supporting critical operations across domains like disaster response, autonomous vehicles, and sensor networks. This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach as a significant step forward in achieving more decentralized, efficient, and collaborative information dissemination. We propose a Partially Observable Stochastic Game (POSG) formulation for information dissemination empowering each agent to decide on message forwarding independently, based on the observation of their one-hop neighborhood. This constitutes a significant paradigm shift from heuristics currently employed in real-world broadcast protocols. Our novel approach harnesses Graph Convolutional Reinforcement Learning and Graph Attention Networks (GATs) with dynamic attention to capture essential network features. We propose two approaches, L-DyAN and HL-DyAN, which differ in terms of the information exchanged among agents. Our experimental results show that our trained policies outperform existing methods, including the state-of-the-art heuristic, in terms of network coverage as well as communication overhead on dynamic networks of varying density and behavior.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 25 Aug 2023.

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References · 21

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9 more
  1. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 175
  2. 2018Graph Convolutional Reinforcement Learningcited by 152
  3. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
  4. 2017Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Gamescited by 96
  5. 2021DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networkscited by 6
  6. 2022Deep Reinforcement Learning for Multi-Agent Interactioncited by 3
  7. 2023DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learningcited by 2
  8. 2023Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environmentscited by 2
  9. 2023DeepADMR: A Deep Learning based Anomaly Detection for MANET Routingcited by 1

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