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A New Reinforcement Learning Algorithm With Fixed Exploration for Semi-Markov Control in Preventive Maintenance

[+] Author Affiliations
Angelo Encapera, Abhijit Gosavi

Missouri University of Science and Technology, Rolla, MO

Paper No. MSEC2017-2880, pp. V003T04A061; 6 pages
doi:10.1115/MSEC2017-2880
From:
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 3: Manufacturing Equipment and Systems
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5074-9
  • Copyright © 2017 by ASME

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.

Copyright © 2017 by ASME

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