Skip to Main content Skip to Navigation
Conference papers

Intelligent Inventory Control: Is Bootstrapping Worth Implementing?

Abstract : The common belief is that using Reinforcement Learning methods (RL) with bootstrapping gives better results than without. However, inclusion of bootstrapping increases the complexity of the RL implementation and requires significant effort. This study investigates whether inclusion of bootstrapping is worth the effort when applying RL to inventory problems. Specifically, we investigate bootstrapping of the temporal difference learning method by using eligibility trace. In addition, we develop a new bootstrapping extension to the Residual Gradient method to supplement our investigation. The results show questionable benefit of bootstrapping when applied to inventory problems. Significance tests could not confirm that bootstrapping had statistically significantly reduced costs of inventory controlled by a RL agent. Our empirical results are based on a variety of problem settings, including demand correlations, demand variances, and cost structures.
Document type :
Conference papers
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download

https://hal.inria.fr/hal-01524959
Contributor : Hal Ifip <>
Submitted on : Friday, May 19, 2017 - 10:43:19 AM
Last modification on : Friday, October 23, 2020 - 4:38:04 PM
Long-term archiving on: : Monday, August 21, 2017 - 12:39:28 AM

File

978-3-642-32891-6_10_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Tatpong Katanyukul, Edwin Chong, William Duff. Intelligent Inventory Control: Is Bootstrapping Worth Implementing?. 7th International Conference on Intelligent Information Processing (IIP), Oct 2012, Guilin, China. pp.58-67, ⟨10.1007/978-3-642-32891-6_10⟩. ⟨hal-01524959⟩

Share

Metrics

Record views

197

Files downloads

291