Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G

作者：Jia-Yi Zhang, Zih-Yi Liu, Yiyang Zhu, Enyu Shi, Bo-Kai Xu, Chau Yuen, D. Niyato, Mérouane Debbah, Shi Jin, Bo Ai, Xue-Min Shen · 发表于：arXiv.org · 年份：2025 · DOI：10.48550/arxiv.2502.05812 · 被引用次数：39 · 研究领域：Computer Science、Engineering、Mathematics

The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive capacity, ultra-low latency, and unparalleled reliability. Wireless distributed networks and multi-agent reinforcement learning (MARL), both of which have evolved from centralized paradigms, are two promising solutions for the great attention. Given their distinct capabilities, such as decentralization and collaborative mechanisms, integrating these two paradigms holds great promise for unleashing the full power of 6G, attracting significant research and development attention. This paper provides a comprehensive study on MARL-assisted wireless distributed networks for 6G. In particular, we introduce the basic mathematical background and evolution of wireless distributed networks and MARL, as well as demonstrate their interrelationships. Subsequently, we analyze different structures of wireless distributed networks from the perspectives of homogeneous and heterogeneous. Furthermore, we introduce the basic concepts of MARL and discuss two typical categories, including model-based and model-free. We then present critical challenges faced by MARL-assisted wireless distributed networks, providing important guidance and insights for actual implementation. We also explore an interplay between MARL-assisted wireless distributed networks and emerging techniques, such as information bottleneck and mirror lea...