mlsanityData to 14 Oct 2026

DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management

arXiv 2603.19621 ↗First posted 20 Mar 2026cs.LG cs.AIcited by 5

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 18 references that are in the map. First posted on arXiv 20 Mar 2026.

Builds on

8 of 18 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Structure-Informed Deep Reinforcement Learning for Inventory Management29 Jul

Other references · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2015Trust Region Policy Optimizationcited by 2,580
  5. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  6. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
  7. 2022Deep Inventory Managementcited by 23

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guaranteescited by 1
  2. 2026OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Itemscited by 1
  3. 2026When to Commit and When to Defer: Maturing Markov Decision Processes under Refining Information and Expiring Opportunitiescited by 0
  4. 2026Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projectioncited by 0
  5. 2026Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARLcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Zero-shot Generalization in Inventory Management: Train, then Estimate and Decide
  2. 2025Classical and Deep Reinforcement Learning Inventory Control Policies for Pharmaceutical Supply Chains with Perishability and Non-Stationarity
  3. 2024A Deep Q-Network Based on Radial Basis Functions for Multi-Echelon Inventory Management
  4. 2024Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
  5. 2024Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
  6. 2024VC Theory for Inventory Policies
  7. 2025Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness
  8. 2025Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization