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

TL;DR

This paper presents a novel multi-agent reinforcement learning framework using graph neural networks for decentralized and efficient information dissemination in dynamic networks, outperforming existing heuristics.

Contribution

It introduces a POSG-based MARL approach with GATs for decentralized decision-making, a paradigm shift from traditional heuristics, and demonstrates superior performance in dynamic network scenarios.

Findings

Outperforms existing heuristics in network coverage

Reduces communication overhead in dynamic networks

Effective in various network densities and behaviors

Abstract

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.