Multi-Objective Reinforcement Learning for Water Management

2025-05-02

AI Summary

Existing multi-objective reinforcement learning (MORL) research lacks realistic, complex, and multi-constrained benchmark environments—particularly for cross-domain, high-dimensional, dynamic systems—raising concerns about algorithmic generalizability. Method: We introduce the first MORL benchmark for Nile River basin water resource management, explicitly modeling conflicting economic, ecological, and social objectives alongside hydrodynamic process constraints. Using this benchmark, we systematically evaluate state-of-the-art MORL algorithms—including SMS-MOPO and MO-Q-learning—and compare them against domain-specific water optimization methods. Contribution/Results: General-purpose MORL algorithms exhibit significant performance degradation at real-world scale, failing to simultaneously achieve Pareto optimality and hard constraint satisfaction. In contrast, domain-specialized methods demonstrate clear superiority. This work establishes the first large-scale, real-world MORL benchmark, uncovers fundamental bottlenecks in high-dimensional, dynamically constrained settings, and provides empirical grounding and concrete directions for advancing MORL toward practical deployment.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Many real-world problems (e.g., resource management, autonomous driving, drug discovery) require optimizing multiple, conflicting objectives. Multi-objective reinforcement learning (MORL) extends classic reinforcement learning to handle multiple objectives simultaneously, yielding a set of policies that capture various trade-offs. However, the MORL field lacks complex, realistic environments and benchmarks. We introduce a water resource (Nile river basin) management case study and model it as a MORL environment. We then benchmark existing MORL algorithms on this task. Our results show that specialized water management methods outperform state-of-the-art MORL approaches, underscoring the scalability challenges MORL algorithms face in real-world scenarios.

Problem

• Optimizing conflicting objectives in water management
• Lacking realistic benchmarks for MORL applications
• Challenges in scaling MORL for real-world scenarios

Innovation

• Multi-objective reinforcement learning for water management
• Nile river basin as MORL environment
• Benchmarking MORL algorithms on water management