Graph Neural Network Meets Multi-Agent Reinforcement Learning: Fundamentals, Applications, and Future Directions
<|reference_start|>Graph Neural Network Meets Multi-Agent Reinforcement Learning: Fundamentals, Applications, and Future Directions: 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.<|reference_end|>
@article{liu2024graph,
    title={Graph Neural Network Meets Multi-Agent Reinforcement Learning:
  Fundamentals, Applications, and Future Directions},
    author={Ziheng Liu, Jiayi Zhang, Enyu Shi, Zhilong Liu, Dusit Niyato, Bo Ai,
  and Xuemin (Sherman) Shen},
    journal={arXiv preprint arXiv:2404.04898},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04898},
    primaryClass={cs.IT math.IT}
}