Multi-Objective Reinforcement Learning for Water Management

TL;DR

This paper introduces a realistic water management environment for multi-objective reinforcement learning, benchmarks existing algorithms, and highlights scalability challenges in complex, real-world scenarios.

Contribution

It provides a new complex environment for MORL and evaluates existing algorithms, revealing their limitations in real-world water management tasks.

Findings

Specialized water management methods outperform general MORL algorithms

Existing MORL algorithms face scalability challenges

Benchmark results highlight the need for more scalable MORL solutions

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.