Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management

Authors: Julen Cestero Portu Marco Quartulli Alberto Maria Metelli Marcello Restelli

Date: 18.07.2022


Abstract

Warehouse Management Systems have been evolving and improving thanks to new Data Intelligence techniques. However, many current optimizations have been applied to specific cases or are in great need of manual interaction. Here is where Reinforcement Learning techniques come into play, providing automatization and adaptability to current optimization policies. In this paper, we present Storehouse, a customizable environment that generalizes the definition of warehouse simulations for Reinforcement Learning. We also validate this environment against state-of-the-art reinforcement learning algorithms and compare these results to human and random policies.

BIB_text

@Article {
title = {Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management},
keywds = {
Training , Industries , Adaptation models , Neural networks , Layout , Reinforcement learning , Manuals
}
abstract = {

Warehouse Management Systems have been evolving and improving thanks to new Data Intelligence techniques. However, many current optimizations have been applied to specific cases or are in great need of manual interaction. Here is where Reinforcement Learning techniques come into play, providing automatization and adaptability to current optimization policies. In this paper, we present Storehouse, a customizable environment that generalizes the definition of warehouse simulations for Reinforcement Learning. We also validate this environment against state-of-the-art reinforcement learning algorithms and compare these results to human and random policies.


}
isbn = {978-1-7281-8671-9},
date = {2022-07-18},
}
Vicomtech

Parque Científico y Tecnológico de Gipuzkoa,
Paseo Mikeletegi 57,
20009 Donostia / San Sebastián (Spain)

+(34) 943 309 230

Edificio Ensanche,
Zabalgune Plaza 11,
48009 Bilbao (Spain)

close overlay