Multi-Objective Reinforcement Learning for Water Management

Zuzanna Osika; Roxana Rădulescu; Jazmin Zatarain Salazar; Frans Oliehoek; Pradeep K. Murukannaiah

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arXiv · 2505.01094

Multi-Objective Reinforcement Learning for Water Management

Zuzanna Osika·Roxana Rădulescu·Jazmin Zatarain Salazar·Frans Oliehoek·Pradeep K. Murukannaiah

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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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Zuzanna Osika, Roxana Rădulescu, Jazmin Zatarain Salazar, Frans Oliehoek, Pradeep K. Murukannaiah. 2025-11-21. Multi-Objective Reinforcement Learning for Water Management. https://arxiv.org/abs/2505.01094

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