Graph Neural Network Meets Multi-Agent Reinforcement Learning: Fundamentals, Applications, and Future Directions

TL;DR

This paper introduces GNNComm-MARL, a graph neural network-enhanced multi-agent reinforcement learning framework for wireless communication, addressing key challenges and demonstrating improved performance in resource allocation and mobility management.

Contribution

It proposes a novel GNN-based MARL architecture with a systematic design, advancing the application of graph attention networks in wireless communication systems.

Findings

GNNComm-MARL outperforms traditional schemes in accuracy and efficiency.

The framework reduces communication overhead significantly.

Effective in resource allocation and mobility management tasks.

Abstract

Multi-agent reinforcement learning (MARL) has become a fundamental component of next-generation wireless communication systems. Theoretically, although MARL has the advantages of low computational complexity and fast convergence rate, there exist several challenges including partial observability, non-stationary, and scalability. In this article, we investigate a novel MARL with graph neural network-aided communication (GNNComm-MARL) to address the aforementioned challenges by making use of graph attention networks to effectively sample neighborhoods and selectively aggregate messages. Furthermore, we thoroughly study the architecture of GNNComm-MARL and present a systematic design solution. We then present the typical applications of GNNComm-MARL from two aspects: resource allocation and mobility management. The results obtained unveil that GNNComm-MARL can achieve better performance with lower communication overhead compared to conventional communication schemes. Finally, several important research directions regarding GNNComm-MARL are presented to facilitate further investigation.