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

Bram J. De Moor, Joren Gijsbrechts, Robert N. Boute

Research output: Indexed journal article Articlepeer-review

32 Citations (Scopus)

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. (c) 2021 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)535-545
Number of pages11
JournalEuropean Journal of Operational Research
Volume301
Issue number2
Early online dateApr 2022
DOIs
Publication statusPublished - 1 Sept 2022
Externally publishedYes

Keywords

  • Deep reinforcement learning
  • Inventory
  • Perishable inventory management
  • Reward shaping
  • Transfer learning

Fingerprint

Dive into the research topics of 'Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management'. Together they form a unique fingerprint.

Cite this