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.

翻译：由于有了新的数据情报技术,仓库管理系统一直在发展和改进,但是,许多目前的优化措施已经应用于具体案例,或非常需要人工互动。这里是“强化学习”技术发挥作用的地方,它为当前的优化政策提供自动化和适应性。本文介绍一个可定制的环境,它概括了“强化学习”仓库模拟的定义。我们还对照最先进的强化学习算法来验证这种环境,并将这些结果与人类和随机政策进行比较。