Masters Theses

Keywords and Phrases

Artificial Intelligence; iSMART; Q-Learning; Reinforcement Learning; RSMART

Abstract

"Artificial intelligence or machine learning techniques are currently being widely applied for solving problems within the field of data analytics. This work presents and demonstrates the use of a new machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in the domain of Reinforcement Learning to solve control problems in discrete-event systems. The new algorithm developed here is called iSMART, an acronym for imaging Semi-Markov Average Reward Technique. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. The major difference between R-SMART and iSMART is that the latter uses, in addition to the regular iterates of R-SMART, a set of so-called imaging iterates, which form an image of the regular iterates and allow iSMART to avoid exploration decay. The new algorithm is tested extensively on small-scale SMDPs and on large-scale problems from the domain of Total Productive Maintenance (TPM). The algorithm shows encouraging performance on all the cases studied"--Abstract, page iii.

Advisor(s)

Gosavi, Abhijit

Committee Member(s)

Enke, David Lee, 1965-
Sun, Zeyi

Department(s)

Engineering Management and Systems Engineering

Degree Name

M.S. in Systems Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2017

Pagination

x, 41 pages

Note about bibliography

Includes bibliographical references (pages 38-40).

Rights

© 2017 Angelo Michael Encapera, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11340

Electronic OCLC #

1041856368

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