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master's thesis
Osnove podržanog učenja
2022. urn:nbn:hr:166:303398
Ozretić, Petar
University of Split
Faculty of Science
Department of Mathematics

Cite this document

Ozretić, P. (2022). Osnove podržanog učenja (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:303398

Ozretić, Petar. "Osnove podržanog učenja." Master's thesis, University of Split, Faculty of Science, 2022. https://urn.nsk.hr/urn:nbn:hr:166:303398

Ozretić, Petar. "Osnove podržanog učenja." Master's thesis, University of Split, Faculty of Science, 2022. https://urn.nsk.hr/urn:nbn:hr:166:303398

Ozretić, P. (2022). 'Osnove podržanog učenja', Master's thesis, University of Split, Faculty of Science, accessed 14 November 2024, https://urn.nsk.hr/urn:nbn:hr:166:303398

Ozretić P. Osnove podržanog učenja [Master's thesis]. Split: University of Split, Faculty of Science; 2022 [cited 2024 November 14] Available at: https://urn.nsk.hr/urn:nbn:hr:166:303398

P. Ozretić, "Osnove podržanog učenja", Master's thesis, University of Split, Faculty of Science, Split, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:166:303398

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Metadata
TitleOsnove podržanog učenja
Title (english)Basics of reinforcement learning
AuthorPetar Ozretić
MentorSaša Mladenović (mentor)
Committee memberSaša Mladenović (predsjednik povjerenstva)
Committee memberAna Perišić (član povjerenstva)
Committee memberVesna Gotovac Đogaš (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Mathematics)
Split
Defense date and country2022-09-21, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
Ovaj se rad bavi osnovama podržanog učenja iznoseći sve poznate metode korištene u PU poput Bellmanovih jednadžbi, Dinamičkog programiranja, teorema poboljšanja strategije,Monte-Carlo učenja, učenja s vremenskom razlikom itd. Osnovni problem je bio nalaženje optimalne strategije uz pomoć optimalne vrijednosne funkcije, i to u okolini bez modela. Kombinacijom navedenih metoda, posebno MC iteracije strategija, napisan je kod koji je uspješno nalazio optimalne strategije na primjeru igre Blackjack.
Abstract (english)
This paper deals with the basics of reinforcement learning, presenting all known methods used in PU such as Bellman equations, Dynamic programming, strategy improvement theorem, MC learning, temporal difference learning, etc. The basic problem was finding an optimal strategy that converges to an optimal value function, and in a model-free environment. By combining the above methods, especially MC strategy iteration, a code was written that successfully found optimal strategies on the example of the Blackjack game.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:303398
Study programmeTitle: Mathematics; specializations in: major in pure mathematics, major in computing, major in teaching, major in statistics and computing
Course: major in computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-11-24 10:04:43