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。

基于图形的多智能体强化学习的协同信息传播学习

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

Learning to cooperate is crucially important in multi-agent environments. The
key is to understand the mutual interplay between agents. However, multi-agent
environments are highly dynamic, where agents keep moving and their neighbors
change quickly. This makes it hard to learn abstract representations of mutual
interplay between agents. To tackle these difficulties, we propose graph
convolutional reinforcement learning, where graph convolution adapts to the
dynamics of the underlying graph of the multi-agent environment, and relation
kernels capture the interplay between agents by their relation representations.
Latent features produced by convolutional layers from gradually increased
receptive fields are exploited to learn cooperation, and cooperation is further
improved by temporal relation regularization for consistency. Empirically, we
show that our method substantially outperforms existing methods in a variety of
cooperative scenarios.

本文提出了一种使用图卷积强化学习的方法，通过使用关系内核捕获代理之间的相互作用来适应多代理环境的动态，并利用逐渐增大感受野的卷积层产生潜在特征来学习合作，此外，为了保持一致性，还使用了时间关系正则化方法。实验表明，该方法在各种合作场景中显着优于现有方法。