| Title: |
Multi-Objective Reinforcement Learning for Water Management |
| Authors: |
Osika, Zuzanna; Rădulescu, Roxana; Zatarain-Salazar, Jazmin; Oliehoek, Frans A.; Murukannaiah, Pradeep K.; Sub Intelligent Systems; Vorobeychik, Yevgeniy; Das, Sanmay; Nowe, Ann |
| Publication Year: |
2025 |
| Subject Terms: |
Multi-Objective Reinforcement Learning; Water Management; Artificial Intelligence; Software; Control and Systems Engineering; SDG 6 - Clean Water and Sanitation |
| Description: |
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. |
| Document Type: |
book part |
| File Description: |
text/plain |
| Language: |
English |
| ISSN: |
1548-8403 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/482995 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/482995 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.D499B61 |
| Database: |
BASE |