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Journal of Applied Mathematics
Volume 2014, Article ID 238357, 8 pages
Research Article

Intelligent Inventory Control via Ruminative Reinforcement Learning

1Faculty of Engineering, Khon Kaen University, Computer Engineering Building, 123 Moo 16, Mitraparb Road, Muang, Khon Kaen 40002, Thailand
2Department of Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523-1373, USA

Received 30 December 2013; Accepted 29 May 2014; Published 7 July 2014

Academic Editor: Aderemi Oluyinka Adewumi

Copyright © 2014 Tatpong Katanyukul and Edwin K. P. Chong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Inventory management is a sequential decision problem that can be solved with reinforcement learning (RL). Although RL in its conventional form does not require domain knowledge, exploiting such knowledge of problem structure, usually available in inventory management, can be beneficial to improving the learning quality and speed of RL. Ruminative reinforcement learning (RRL) has been introduced recently based on this approach. RRL is motivated by how humans contemplate the consequences of their actions in trying to learn how to make a better decision. This study further investigates the issues of RRL and proposes new RRL methods applied to inventory management. Our investigation provides insight into different RRL characteristics, and our experimental results show the viability of the new methods.