MARLIN: Multi-Agent Reinforcement Learning with Murmuration Intelligence and LLM Guidance for Reservoir Management

2025-09-29

AI Summary

Addressing cascading uncertainties—induced by evaporation, leakage, and multi-stakeholder coordination failures—in reservoir networks under climate change, this paper proposes a decentralized multi-agent reinforcement learning (MARL) framework inspired by avian swarm intelligence. It is the first to integrate biologically inspired flocking rules (alignment, separation, cohesion) with a large language model (LLM)-driven dynamic reward shaping mechanism. The method enables distributed, adaptive decision-making and is validated on USGS real-world data: it improves uncertainty modeling accuracy by 23%, reduces computational overhead by 35%, accelerates flood response by 68%, and achieves superlinear scalability across network sizes of 400–10,000 nodes. The core innovation lies in an LLM-guided, real-time reward modulation coupled with swarm-intelligence-informed decentralized control—a paradigm that significantly overcomes scalability and robustness limitations of conventional centralized optimization and existing MARL approaches in complex water infrastructure systems.

Technology Category

• Multiagent Systems: Multiagent Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As climate change intensifies extreme weather events, water disasters pose growing threats to global communities, making adaptive reservoir management critical for protecting vulnerable populations and ensuring water security. Modern water resource management faces unprecedented challenges from cascading uncertainties propagating through interconnected reservoir networks. These uncertainties, rooted in physical water transfer losses and environmental variability, make precise control difficult. For example, sending 10 tons downstream may yield only 8-12 tons due to evaporation and seepage. Traditional centralized optimization approaches suffer from exponential computational complexity and cannot effectively handle such real-world uncertainties, while existing multi-agent reinforcement learning (MARL) methods fail to achieve effective coordination under uncertainty. To address these challenges, we present MARLIN, a decentralized reservoir management framework inspired by starling murmurations intelligence. Integrating bio-inspired alignment, separation, and cohesion rules with MARL, MARLIN enables individual reservoirs to make local decisions while achieving emergent global coordination. In addition, a LLM provides real-time reward shaping signals, guiding agents to adapt to environmental changes and human-defined preferences. Experiments on real-world USGS data show that MARLIN improves uncertainty handling by 23%, cuts computation by 35%, and accelerates flood response by 68%, exhibiting super-linear coordination, with complexity scaling 5.4x from 400 to 10,000 nodes. These results demonstrate MARLIN's potential for disaster prevention and protecting communities through intelligent, scalable water resource management.

Problem

• Addresses cascading uncertainties in interconnected reservoir networks management
• Overcomes computational complexity of traditional centralized optimization approaches
• Enables decentralized coordination under environmental variability using bio-inspired rules

Innovation

• Decentralized reservoir management using bio-inspired rules
• Multi-agent reinforcement learning with emergent coordination
• Real-time reward shaping with LLM guidance