A New Reinforcement Learning Algorithm with Fixed Exploration for Semi-Markov Control in Preventive Maintenance
Abstract
Artificial intelligence techniques can play a significant role in solving problems encountered in the domain of Total Productive Maintenance (TPM). This paper considers a new reinforcement learning algorithm called iSMART, which can solve semi-Markov decision processes underlying control problems related to TPM. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. Numerical experiments conducted here show encouraging behavior with the new algorithm.
Recommended Citation
A. Encapera and A. Gosavi, "A New Reinforcement Learning Algorithm with Fixed Exploration for Semi-Markov Control in Preventive Maintenance," Proceedings of the ASME 2017 12th International Manufacturing Science and Engineering Conference (2017, Los Angeles, CA), vol. 3, American Society of Mechanical Engineers (ASME), Jun 2017.
The definitive version is available at https://doi.org/10.1115/MSEC2017-2880
Meeting Name
ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 (2017: Jun. 4-8, Los Angeles, CA)
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Manufacture; Markov processes; Preventive maintenance; Problem solving; Reinforcement learning; Artificial intelligence techniques; Control problems; Numerical experiments; Semi-Markov controls; Semi-Markov decision process; Total productive maintenance; Learning algorithms
International Standard Book Number (ISBN)
978-0-7918-5074-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 American Society of Mechanical Engineers (ASME), All rights reserved.
Publication Date
01 Jun 2017
Comments
This research was partially funded by a seed grant from the Department of Engineering Management and Systems Engineering at the Missouri University of Science and Technology.