mlsanityData to 14 Oct 2026

Multi-Objective Reinforcement Learning for Water Management

arXiv 2505.01094 ↗First posted 2 May 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 24 references that are in the map. First posted on arXiv 2 May 2025.

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3 of 24 references.

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  1. 2024Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning7 Nov

Other references · 2

  1. 2023Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritizationcited by 48
  2. 2018Urban Driving with Multi-Objective Deep Reinforcement Learningcited by 30

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