In modern communication systems, efficient and reliable 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 solutions. We propose a Partially Observable Stochastic Game (POSG) formulation for information dissemination empowering each agent to decide on message forwarding independently, based on their one-hop neighborhood. This constitutes a significant paradigm shift from traditional heuristics based on Multi-Point Relay (MPR) selection. Our approach harnesses Graph Convolutional Reinforcement Learning, employing Graph Attention Networks (GAT) with dynamic attention to capture essential network features. We propose two approaches, L-DGN and HL-DGN, which differ in the information that is exchanged among agents. We evaluate the performance of our decentralized approaches, by comparing them with a widely-used MPR heuristic, and we show that our trained policies are able to efficiently cover the network while bypassing the MPR set selection process. Our approach is a first step toward supporting the resilience of real-world broadcast communication infrastructures via learned, collaborative information dissemination.

翻译：在现代通信系统中，高效可靠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关键领域至关重要。本文提出了一种多智能体强化学习方法，在实现更分散、更高效和协作的解决方案方面迈出了重要一步。我们提出了一种部分可观测随机博弈模型用于信息传播，使每个智能体能够根据其一跳邻域独立决定消息转发。这构成了从基于多点中继选择的传统启发式方法的重大范式转变。我们的方法利用图卷积强化学习，采用具有动态注意力机制的图注意力网络来捕捉关键网络特征。我们提出了两种方法L-DGN和HL-DGN，它们在智能体间交换的信息上有所不同。通过将我们提出的分散式方法与广泛使用的MPR启发式方法进行比较，我们评估了其性能，结果表明我们训练的策略能够有效覆盖网络，同时绕过MPR集合选择过程。我们的方法为通过学习型协作信息传播来支持真实世界广播通信基础设施的弹性迈出了第一步。