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

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

Producción científica: Artículo en revista indizadaArtículorevisión exhaustiva

32 Citas (Scopus)

Resumen

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.
Idioma originalInglés
Páginas (desde-hasta)535-545
Número de páginas11
PublicaciónEuropean Journal of Operational Research
Volumen301
N.º2
Fecha en línea anticipadaabr 2022
DOI
EstadoPublicada - 1 sept 2022
Publicado de forma externa

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