Multi-Objective Reinforcement Learning for Water Management
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912721582161920 |
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| author | Osika, Zuzanna Rădulescu, Roxana Salazar, Jazmin Zatarain Oliehoek, Frans Murukannaiah, Pradeep K. |
| author_facet | Osika, Zuzanna Rădulescu, Roxana Salazar, Jazmin Zatarain Oliehoek, Frans Murukannaiah, Pradeep K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01094 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Objective Reinforcement Learning for Water Management Osika, Zuzanna Rădulescu, Roxana Salazar, Jazmin Zatarain Oliehoek, Frans Murukannaiah, Pradeep K. Machine Learning Artificial Intelligence 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. |
| title | Multi-Objective Reinforcement Learning for Water Management |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.01094 |