  Deep Reinforcement Learning (DRL) has achieved remarkable success in
scenarios such as games and has emerged as a potential solution for control
tasks. That is due to its ability to leverage scalability and handle complex
dynamics. However, few works have targeted environments grounded in real-world
settings. Indeed, real-world scenarios can be challenging, especially when
faced with the high dimensionality of the state space and unknown reward
function. We release a testbed consisting of an environment simulator and
demonstrations of human operation concerning pump scheduling of a real-world
water distribution facility to facilitate research. The pump scheduling problem
can be viewed as a decision process to decide when to operate pumps to supply
water while limiting electricity consumption and meeting system constraints. To
provide a starting point, we release a well-documented codebase, present an
overview of some challenges that can be addressed and provide a baseline
representation of the problem. The code and dataset are available at
https://gitlab.com/hdonancio/pumpscheduling.
