Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management
This work provides a tool for researchers and practitioners to optimize warehouse operations, but it is incremental as it builds on existing reinforcement learning methods.
The authors tackled the problem of automating warehouse management by introducing Storehouse, a customizable reinforcement learning environment for warehouse simulations, and validated it against state-of-the-art algorithms, showing improved performance over human and random policies.
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.