Multi-Agent Reinforcement Learning (MARL) has become a powerful framework for numerous real-world applications, modeling distributed decision-making and learning from interactions with complex environments. Resource Allocation Optimization (RAO) benefits significantly from MARL's ability to tackle dynamic and decentralized contexts. MARL-based approaches are increasingly applied to RAO challenges across sectors playing pivotal roles to Industry 4.0 developments. This survey provides a comprehensive review of recent MARL algorithms for RAO, encompassing core concepts, classifications, and a structured taxonomy. By outlining the current research landscape and identifying primary challenges and future directions, this survey aims to support researchers and practitioners in leveraging MARL's potential to advance resource allocation solutions.

本文解决了多智能体强化学习（MARL）在资源分配优化（RAO）中应用的研究空白。通过综述最近的MARL算法，本文提出了一个结构化的分类法，并指出当前研究中的主要挑战及未来方向，旨在帮助研究者和从业者更好地利用MARL的潜力来改善资源分配解决方案。研究结果显示，MARL在动态和分散决策场景中对RAO的显著促进作用。

多智能体强化学习在资源分配优化中的应用综述