Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909597116137472 |
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| author | Hady, Mohamad A. Hu, Siyi Pratama, Mahardhika Cao, Jimmy Kowalczyk, Ryszard |
| author_facet | Hady, Mohamad A. Hu, Siyi Pratama, Mahardhika Cao, Jimmy Kowalczyk, Ryszard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey Hady, Mohamad A. Hu, Siyi Pratama, Mahardhika Cao, Jimmy Kowalczyk, Ryszard Multiagent Systems Artificial Intelligence Machine Learning 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. |
| title | Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey |
| topic | Multiagent Systems Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.21048 |