The ever-increasing demand for high-quality and heterogeneous wireless communication services has driven extensive research on dynamic optimization strategies in wireless networks. Among several possible approaches, multi-agent deep reinforcement learning (MADRL) has emerged as a promising method to address a wide range of complex optimization problems like power control. However, the seamless application of MADRL to a variety of network optimization problems faces several challenges related to convergence. In this paper, we present the use of graphs as communication-inducing structures among distributed agents as an effective means to mitigate these challenges. Specifically, we harness graph neural networks (GNNs) as neural architectures for policy parameterization to introduce a relational inductive bias in the collective decision-making process. Most importantly, we focus on modeling the dynamic interactions among sets of neighboring agents through the introduction of innovative methods for defining a graph-induced framework for integrated communication and learning. Finally, the superior generalization capabilities of the proposed methodology to larger networks and to networks with different user categories is verified through simulations.

翻译：随着对高质量异构无线通信服务需求的持续增长，针对无线网络动态优化策略的研究日益深入。在多类可行方案中，多智能体深度强化学习已成为解决功率控制等复杂优化问题的有力工具。然而，多智能体深度强化学习在各类网络优化问题中的无缝应用仍面临收敛性相关挑战。本文提出采用图结构作为分布式智能体间通信诱导机制，以有效缓解此类挑战。具体而言，我们利用图神经网络作为策略参数化的神经架构，在集体决策过程中引入关系归纳偏置。尤为重要的是，我们聚焦于通过创新方法建模邻近智能体集合间的动态交互，构建基于图结构的通信与学习融合框架。最后，通过仿真验证了所提方法在更大规模网络及不同用户类别网络中的优异泛化能力。