Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
It addresses the need for efficient resource allocation in dynamic, decentralized contexts like Industry 4.0, but is incremental as it compiles existing research without new results.
This survey reviews Multi-Agent Reinforcement Learning (MARL) algorithms for Resource Allocation Optimization (RAO), summarizing core concepts, classifications, and challenges to support researchers and practitioners in advancing solutions.
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.