综述多智能体强化学习在资源分配中的应用与进展
Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
- 系统梳理多智能体强化学习用于资源分配的算法框架
- 归纳现有方法分类，构建资源分配研究的结构化体系
- 适合关注工业4.0、分布式优化的研究者参考
多智能体强化学习（MARL）已成为众多现实应用的强大框架，能够建模分布式决策并从复杂环境交互中学习。资源分配优化（RAO）显著受益于MARL在动态和去中心化场景中的处理能力。基于MARL的方法正日益应用于各行业的资源分配挑战，对工业4.0发展起关键作用。本综述全面回顾了近期用于RAO的MARL算法，涵盖核心概念、分类与结构化分类体系。通过梳理当前研究现状，识别主要挑战与未来方向，旨在帮助研究人员与从业者充分发挥MARL潜力，推动资源分配解决方案的演进。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。