Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning
CoRR(2023)
摘要
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.
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关键词
collaborative information dissemination,reinforcement learning,graph-based,multi-agent
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