DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management

References

• Achiam J (2018) Spinning up in deep reinforcement learning. Accessed January 1, 2024, https://spinningup.openai.com/en/latest/spinningup/rl_intro.html.Google Scholar
• Alvo M, Russo D, Kanoria Y (2023) Neural inventory control in networks via hindsight differentiable policy optimization. Preprint, submitted June 23, https://arxiv.org/abs/2306.11246v1.Google Scholar
• De Moor BJ, Gijsbrechts J, Boute RN (2022) Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management. Eur. J. Oper. Res. 301(2):535–545.Google Scholar
• Gijsbrechts J, Boute RN, Van Mieghem JA, Zhang D (2025) AI in inventory management: The disruptive era of DRL and beyond. Preprint, submitted March 31, https://doi.org/10.2139/ssrn.5199616.Google Scholar
• Haarnoja T, Zhou A, Abbeel P, Levine S (2018) Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. Proc. 35th Internat. Conf. Machine Learn., vol. 80 (PMLR, New York), 1861–1870.Google Scholar
• Liaw R, Liang E, Nishihara R, Moritz P, Gonzalez JE, Stoica I (2018) Tune: A research platform for distributed model selection and training. Preprint, submitted July 13, https://arxiv.org/abs/1807.05118.Google Scholar
• Lillicrap TP, Hunt JJ, Pritzel A, Heess N, Erez T, Tassa Y, Silver D, Wierstra D (2015) Continuous control with deep reinforcement learning. Preprint, submitted September 9, https://arxiv.org/abs/1509.02971v1.Google Scholar
• Liu J, Lin S, Xin L, Zhang Y (2023) AI vs. human buyers: A study of Alibaba’s inventory replenishment system. INFORMS J. Appl. Anal. 53(5):372–387.Link, Google Scholar
• Madeka D, Torkkola K, Eisenach C, Luo A, Foster DP, Kakade SM (2022) Deep inventory management. Preprint, submitted November 28, https://arxiv.org/abs/2210.03137.Google Scholar
• Maggiar A, Andaz S, Bagaria A, Eisenach C, Foster D, Gottesman O, Perrault-Joncas D (2025) Structure-informed deep reinforcement learning for inventory management. Preprint, submitted July 29, https://arxiv.org/abs/2507.22040.Google Scholar
• Qi M, Shi Y, Qi Y, Ma C, Yuan R, Wu D, Shen Z-J (2023) A practical end-to-end inventory management model with deep learning. Management Sci. 69(2):759–773.Link, Google Scholar
• Raffin A, Hill A, Gleave A, Kanervisto A, Ernestus M, Dormann N (2021) Stable-Baselines3: Reliable reinforcement learning implementations. J. Machine Learn. Res. 22(268):1–8.Google Scholar
• Schulman J, Levine S, Abbeel P, Jordan M, Moritz P (2015a) Trust region policy optimization. Proc. 32nd Internat. Conf. Machine Learn., vol. 37 (PMLR, New York), 1889–1897.Google Scholar
• Schulman J, Moritz P, Levine S, Jordan M, Abbeel P (2015b) High-dimensional continuous control using generalized advantage estimation. Preprint, submitted June 8, https://arxiv.org/abs/1506.02438v1.Google Scholar
• Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. Preprint, submitted August 28, https://arxiv.org/abs/1707.06347.Google Scholar
• Snyder LV, Shen Z-JM (2019) Fundamentals of Supply Chain Theory. 2nd ed. (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Vanvuchelen N, De Moor BJ, Boute RN (2025) The use of continuous action representations to scale deep reinforcement learning for inventory control. IMA J. Management Math. 36(1):51–66.Google Scholar
• Xie Y, Ma W, Xin L (2026) VC theory for inventory policies. Preprint, submitted January 31, https://doi.org/10.2139/ssrn.4794903.Google Scholar