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

TL;DR

The paper tackles the challenge of centralized reservoir management struggling with computational complexity and uncertainty propagation in large, interconnected networks. It proposes MurmuRL, a decentralized, Murmuration-guided MARL framework that embeds bio-inspired alignment, separation, and cohesion to achieve emergent global coordination from local decisions. Empirical results on grid networks show MurmuRL outperforms traditional MARL baselines, achieving 8.8% higher final performance with 27% reduced computing overhead and exhibiting super-linear scaling in strategy diversity as system size grows. The approach promises scalable, robust water resource management by leveraging simple local rules to orchestrate complex, resilient collective behavior under uncertainty.

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.

Figures (6)

• Figure 1: Water resource management challenges: (a) complex networks of interconnected reservoirs requiring coordination, (b) individual reservoirs that must be locally managed, and (c) high global flood frequency demanding more robust management systems flood_displacement.
• Figure 2: Starling Murmuration: A natural example of emergent intelligence from simple local interaction rules, where thousands of birds create complex, responsive patterns without centralized coordination. (Photo by Menno Schaefner schaefner_murmuration)
• Figure 3: Reservoir node modeling in a grid-based hydrological network. Each node (e.g., node A) interacts with four neighboring nodes, managing water flows in a general pattern from upstream (top-left) to downstream (bottom-right).
• Figure 4: Cluster formation comparison at different scales. The Murmuration approach (left images) generates significantly more distinct clusters than the baseline approach (right images) at both scales, with the advantage becoming more pronounced at the larger scale. Colors represent water management strategies with values between approximately 7 and 13 units.
• Figure 5: Radar chart comparing performance across five metrics. MurmuRL (blue) shows balanced superior performance compared to baselines.

Theorems & Definitions (1)

• Remark 1