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Warehouse Management Systems have been evolving and improving thanks to newData Intelligence techniques. However, many current optimizations have beenapplied to specific cases or are in great need of manual interaction. Here iswhere Reinforcement Learning techniques come into play, providingautomatization and adaptability to current optimization policies. In thispaper, we present Storehouse, a customizable environment that generalizes thedefinition of warehouse simulations for Reinforcement Learning. We alsovalidate this environment against state-of-the-art reinforcement learningalgorithms and compare these results to human and random policies.

Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management