Data-driven inventory management for new products: A warm-start and adjusted Dyna-$Q$ approach

-- In this paper, we propose a novel reinforcement learning algorithm for inventory management of newly launched products with no historical demand information. The algorithm follows the classic Dyna-Q structure, balancing the model-free and model-based approaches, while accelerating the training process of Dyna-Q and mitigating the model discrepancy generated by the model-based feedback. Based on the idea of transfer learning, warm-start information from the demand data of existing similar products can be incorporated into the algorithm to further stabilize the early-stage training and reduce the variance of the estimated optimal policy. Our approach is validated through a case study of bakery inventory management with real data. The adjusted Dyna-Q shows up to a 23.7% reduction in average daily cost compared with Q-learning, and up to a 77.5% reduction in training time within the same horizon compared with classic Dyna-Q . By using transfer learning, it can be found that the adjusted Dyna-Q has the lowest total cost, lowest variance in total cost, and relatively low shortage percentages among all the benchmarking algorithms under a 30-day testing. I. INTRODUCTION Inventory management is crucial for supply chain operations, overseeing and controlling the order, storage, and usage of goods in business [1]. In inventory management, the cold-start setting refers to predicting demand and formulating appropriate inventory strategies when new products are introduced or new market demands arise due to the lack of historical data [2].