[2603.19621] DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management

Computer Science > Machine Learning arXiv:2603.19621 (cs) [Submitted on 20 Mar 2026] Title:DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management Authors:Yaqi Xie, Xinru Hao, Jiaxi Liu, Will Ma, Linwei Xin, Lei Cao, Yidong Zhang View a PDF of the paper titled DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management, by Yaqi Xie and 6 other authors View PDF HTML (experimental) 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 accelerate hyperparameter tuning and improve the final performance of several DRL methods. We report details from a 100% deployment of DRL with policy regularizations on Alibaba's e-commerce platform, Tmall. We also include extensive synthetic experiments, which show that policy regularizations reshape the narrative on what is the best DRL method for inventory management. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.19621 [cs.LG]   (or arXiv:2603.19621v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.19621 Focus to learn more arXiv-issued...