An adjusted Dyna-Q algorithm with decaying exploration and transfer learning is shown to reduce cost and training time in a simulated cold-start inventory problem.
Inventory management of new products in retailers using model-based deep reinforcement learning,
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Data-driven inventory management for new products: An adjusted Dyna-$Q$ approach with transfer learning
An adjusted Dyna-Q algorithm with decaying exploration and transfer learning is shown to reduce cost and training time in a simulated cold-start inventory problem.