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 Decentralized-POMDP formulation for
information dissemination, empowering each agent to independently decide on
message forwarding. 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 promises a first step
toward bolstering the resilience of real-world broadcast communication
infrastructures via learned, collaborative information dissemination.

利用多智能体强化学习的分散式 POMDP 方法，通过图卷积强化学习和动态注意力技术实现信息传播，提供了可靠的协作信息传播解决方案。