Deep Reinforcement Learning (DRL) has demonstrated impressive results in domains such as games and robotics, where task formulations are well-defined. However, few DRL benchmarks are grounded in complex, real-world environments, where safety constraints, partial observability, and the need for hand-engineered task representations pose significant challenges. To help bridge this gap, we introduce a testbed based on the pump scheduling problem in a real-world water distribution facility. The task involves controlling pumps to ensure a reliable water supply while minimizing energy consumption and respecting the constraints of the system. Our testbed includes a realistic simulator, three years of high-resolution (1-minute) operational data from human-led control, and a baseline RL task formulation. This testbed supports a wide range of research directions, including offline RL, safe exploration, inverse RL, and multi-objective optimization.
View on arXiv@article{donâncio2025_2210.11111,
title={ The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning },
author={ Henrique Donâncio and Laurent Vercouter and Harald Roclawski },
journal={arXiv preprint arXiv:2210.11111},
year={ 2025 }
}