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    Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management

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    Publication type
    Vlerick strategic journal article
    Author
    De Moor, Bram J.
    Gijsbrechts, Joren
    Boute, Robert
    Publication Year
    2022
    Journal
    European Journal of Operational Research
    Publication Volume
    301
    Publication Issue
    2
    Publication Begin page
    535
    Publication End page
    545
    
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    Abstract
    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.
    Keyword
    Inventory, Perishable Inventory Management, Deep Reinforcement Learning, Reward Shaping, Transfer Learning
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.1016/j.ejor.2021.10.045
    URI
    http://hdl.handle.net/20.500.12127/6986
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ejor.2021.10.045
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