期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 301, 期 2, 页码 535-545出版社
ELSEVIER
DOI: 10.1016/j.ejor.2021.10.045
关键词
Inventory; Perishable inventory management; Deep reinforcement learning; Reward shaping; Transfer learning
This study demonstrates the feasibility of applying transfer learning to deep reinforcement learning for improving performance and training stability in inventory management. Additionally, potential-based reward shaping is implemented to manage inventory control efficiently.
Deep reinforcement learning (DRL) has proven to be an effective, general-purpose technology to develop 'good' replenishment policies in inventory management. We show how transfer learning from existing, well-performing heuristics may stabilize the training process and improve the performance of DRL in inventory control. While the idea is general, we specifically implement potential-based reward shaping to a deep Q-network algorithm to manage inventory of perishable goods that, cursed by dimensionality, has proven to be notoriously complex. The application of our approach may not only improve inventory cost performance and reduce computational effort, the increased training stability may also help to gain trust in the policies obtained by black box DRL algorithms. (c) 2021 Elsevier B.V. All rights reserved.
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