Multi-Objective Reinforcement Learning for Water Management

• URL: http://arxiv.org/abs/2505.01094v1
• Date: Fri, 02 May 2025 08:14:01 GMT
• Title: Multi-Objective Reinforcement Learning for Water Management
• Authors: Zuzanna Osika, Roxana Radelescu, Jazmin Zatarain Salazar, Frans Oliehoek, Pradeep K. Murukannaiah,
• Abstract summary: Multi-objective reinforcement learning (MORL) extends classic reinforcement learning to handle multiple objectives simultaneously.<n>We introduce a water resource (Nile river basin) management case study and model it as a MORL environment.<n>We then benchmark existing MORL algorithms on this task.<n>Our results show that specialized water management methods outperform state-of-the-art MORL approaches.
• Score: 0.8163897333839454
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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