Title: The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning
Primary area: datasets and benchmarks
Keywords: Reinforcement Learning, Offline RL, Pump Scheduling, Water Distribution Networks

Abstract: 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 adhering to the system's constraints. 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.

TL;DR: We introduce a reinforcement learning testbed based on pump scheduling in a water distribution system, featuring a simulator, human operation data, and a baseline task formulation.