Multi-agent reinforcement learning for resources allocation optimization: a survey
Mohamad Abdul Hady, Siyi Hu, Mahardhika Pratama, Zehong Cao, Ryszard Kowalczyk
Abstract
Abstract 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 a pivotal role in industry 4.0 developments. This survey provides a comprehensive review of recent MARL algorithms for RAO, encompassing core concepts, classifications, design steps and benchmarks. 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.