DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management

2026-03-19

AI Summary

This work addresses the instability and tuning difficulties commonly encountered in deep reinforcement learning (DRL) for inventory management, which often stem from high sensitivity to hyperparameters. To mitigate these challenges, the authors propose a policy regularization method grounded in classical inventory theory—such as base-stock policies—that effectively incorporates domain-specific prior knowledge into the DRL framework. This integration substantially enhances both training efficiency and algorithmic robustness. Empirical evaluations demonstrate consistent and significant performance improvements across multiple DRL algorithms. Notably, the proposed approach has been fully deployed (100% adoption) on Tmall, a large-scale e-commerce platform, thereby validating its effectiveness and practical utility in real-world, industrial-scale inventory management scenarios.

Technology Category

• Machine Learning: Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Inventory Management
• Deep Reinforcement Learning
• Hyperparameter Sensitivity
• Policy Regularization
• Base Stock

Innovation

• Policy Regularization
• Deep Reinforcement Learning
• Inventory Management
• Base Stock Policy
• Hyperparameter Tuning