论文标题
仓库：优化仓库管理的增强学习环境
Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management
论文作者
论文摘要
由于新的数据智能技术，仓库管理系统一直在不断发展和改进。但是，许多当前的优化已应用于特定情况，或者非常需要手动相互作用。这是强化学习技术发挥作用的地方，提供自动化和适应当前优化政策的能力。在本文中，我们介绍了一个可自定义的环境，它概括了用于强化学习的仓库模拟的定义。我们还验证了这种环境，以防止最新的增强学习算法，并将这些结果与人类和随机政策进行比较。
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.
