  Warehouse Management Systems have been evolving and improving thanks to new
Data Intelligence techniques. However, many current optimizations have been
applied to specific cases or are in great need of manual interaction. Here is
where Reinforcement Learning techniques come into play, providing
automatization and adaptability to current optimization policies. In this
paper, we present Storehouse, a customizable environment that generalizes the
definition of warehouse simulations for Reinforcement Learning. We also
validate this environment against state-of-the-art reinforcement learning
algorithms and compare these results to human and random policies.

Machine Learning;Artificial Intelligence