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.

翻译：高效的信息传播对于支持灾难响应、自动驾驶车辆和传感器网络等领域的关键操作至关重要。本文提出了一种基于多智能体强化学习（MARL）的方法，作为在实现更去中心化、高效和协同信息传播方面的重要进展。我们提出了一种部分可观测随机博弈（POSG）框架用于信息传播，使每个智能体能够基于对单跳邻域的观测独立决定消息转发。这构成了对当前实际广播协议中采用启发式方法的重大范式转变。我们的新颖方法利用图卷积强化学习和动态注意力图注意力网络（GATs）来捕获关键网络特征。我们提出了两种方法：L-DyAN和HL-DyAN，它们在学习智能体之间交换的信息类型上有所不同。实验结果表明，在动态网络的不同密度和行为下，我们训练的策略在网络覆盖率和通信开销方面优于现有方法，包括最先进的启发式方法。