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Multi-Objective Reinforcement Learning for Water Management

Zuzanna Osika, Roxana Rădulescu, Jazmin Zatarain-Salazar, Frans A Oliehoek, and Pradeep K Murukannaiah. Multi-Objective Reinforcement Learning for Water Management. In Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, pp. 2702–2704, 2025.

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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.

BibTeX Entry

@inproceedings{Osika25AAMAS,
    title={Multi-Objective Reinforcement Learning for Water Management},
    author={Osika, Zuzanna and R{\u a}dulescu, Roxana and Zatarain-Salazar, Jazmin and Oliehoek, Frans A and Murukannaiah, Pradeep K},
    booktitle = AAMAS25,
    booktitle={Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems},
    pages={2702--2704},
    year={2025},
    organization={IFAAMAS},
    keywords =  {refereed},
    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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