Papers › Newsvendor Model with Deep Reinforcement Learning

Newsvendor Model with Deep Reinforcement Learning

22 Dec 2021arXiv:2112.12544archive 2025-07-28

Dylan K. Goetting

I present a deep reinforcement learning (RL) solution to the mathematical problem known as the Newsvendor model, which seeks to optimize profit given a probabilistic demand distribution. To reflect a more realistic and complex situation, the demand distribution can change for different days of the week, thus changing the optimum behavior. I used a Twin-Delayed Deep Deterministic Policy Gradient agent (written as completely original code) with both an actor and critic network to solve this problem. The agent was able to learn optimal behavior consistent with the analytical solution of the problem, and could identify separate probability distributions for different days of the week and behave accordingly.

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Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)model reinforcement-learning

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