Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning
This addresses the need for decentralized and collaborative information dissemination in domains like disaster response and autonomous vehicles, representing a significant paradigm shift from current heuristics.
The paper tackled the problem of efficient information dissemination in dynamic networks by proposing a Multi-Agent Reinforcement Learning approach with Graph Convolutional Reinforcement Learning and Graph Attention Networks, achieving improved network coverage and reduced communication overhead compared to existing methods.
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.