DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management

Excerpt

arXiv:2603.19621v2 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) provides a general-purpose methodology for training inventory policies that can leverage big data and compute. However, off-the-shelf implementations of DRL have seen mixed success, often plagued by high sensitivity to the hyperparameters used during training. In this paper, we show that by imposing policy regularizations, grounded in classical inventory concepts such as "Base Stock", we can significantly