# Multi-Objective Reinforcement Learning for Water Management

arXiv: https://arxiv.org/abs/2505.01094 · HTML: https://mlsanity.com/paper/2505.01094/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 3 of 3)

- [Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization](https://mlsanity.com/paper/2301.07784/) (2023)
- [Urban Driving with Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1811.08586/) (2018)
- [Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2411.04784/) (2024)

## 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.