A New Reinforcement Learning Algorithm with Fixed Exploration for Semi-Markov Decision Processes
Başlık:
A New Reinforcement Learning Algorithm with Fixed Exploration for Semi-Markov Decision Processes
Yazar:
Encapera, Angelo Michael, author.
ISBN:
9780438115859
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (52 pages)
Genel Not:
Source: Masters Abstracts International, Volume: 57-06M(E).
Advisors: Abhijit Gosavi Committee members: David Enke; Zeyi Sun.
Özet:
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.
Notlar:
School code: 0587
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(688052.1) | 688052-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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