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arXiv · 2504.11569

Multi-Agent Reinforcement Learning for Decentralized Reservoir Management via Murmuration Intelligence

Heming Fu·Guojun Xiong·Jian Li·Shan Lin
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Abstract

Conventional centralized water management systems face critical limitations from computational complexity and uncertainty propagation. We present MurmuRL, a novel decentralized framework inspired by starling murmurations intelligence, integrating bio-inspired alignment, separation, and cohesion rules with multi-agent reinforcement learning. MurmuRL enables individual reservoirs to make autonomous local decisions while achieving emergent global coordination. Experiments on grid networks demonstrate that MurmuRL achieves 8.8% higher final performance while using 27% less computing overhead compared to centralized approaches. Notably, strategic diversity scales super-linearly with system size, exhibiting sophisticated coordination patterns and enhanced resilience during extreme events. MurmuRL offers a scalable solution for managing complex water systems by leveraging principles of natural collective behavior.

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Heming Fu, Guojun Xiong, Jian Li, Shan Lin. 2025-04-15. Multi-Agent Reinforcement Learning for Decentralized Reservoir Management via Murmuration Intelligence. https://arxiv.org/abs/2504.11569

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