Multi-Objective Reinforcement Learning for Water Management

TL;DR

The paper addresses evaluating MORL methods in a realistic, high-dimensional water-management task. It casts the Nile River Basin problem as a MOMDP using the MO-Gymnasium API and benchmarks existing MORL algorithms against the specialized EMODPS method. The results show that domain-specific water-management methods can outperform generic MORL approaches and reveal scalability and exploration gaps in current MORL algorithms when faced with real-world objectives and large state/action spaces. This work provides a realistic benchmark to drive MORL research toward practical, high-impact deployments in water resources.

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.

Figures (1)

• Figure 1: Parallel coordinate plots with solution sets achieved by each algorithm for the Nile environment.