Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey

Mohamad A. Hady, Siyi Hu, Mahardhika Pratama, Jimmy Cao, Ryszard Kowalczyk
4/29/2025
cs.MAcs.AIcs.LG

No official code found. Best implementation: AVSLab/bsk_rl (122 stars, MIT, last commit 2026-04-28).

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 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.

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Code Implementations(1)

AVSLab/bsk_rlCommunity100%
MIT

RL environments and tools for spacecraft autonomy research, built on Basilisk. Developed by the AVS Lab.

12219PythonJun 8, 20235 months agoMIT
autonomyearth-observationreinforcement-learningsatellite-tasking

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Cite this paper

@article{hady2025multiagent,
  title  = {Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey},
  author = {Mohamad A. Hady and Siyi Hu and Mahardhika Pratama and Jimmy Cao and Ryszard Kowalczyk},
  year   = {2025},
  eprint = {2504.21048},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.21048v1}
}

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