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undergraduate thesis
Primjena podržanog učenja kod igara
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:769026
Puhalović, Mateo
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Puhalović, M. (2019). Primjena podržanog učenja kod igara (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:769026

Puhalović, Mateo. "Primjena podržanog učenja kod igara." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:769026

Puhalović, Mateo. "Primjena podržanog učenja kod igara." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:769026

Puhalović, M. (2019). 'Primjena podržanog učenja kod igara', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:769026

Puhalović M. Primjena podržanog učenja kod igara [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 November 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:769026

M. Puhalović, "Primjena podržanog učenja kod igara", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:769026

Metadata
TitlePrimjena podržanog učenja kod igara
Title (english)Reinforcement Learning Applied to Games
AuthorMateo Puhalović
MentorTomislav Pribanić (mentor)
Committee memberTomislav Pribanić (predsjednik povjerenstva)
Committee memberKristijan Bartol (član povjerenstva)
Committee memberDavid Bojanić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-09-04, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Podržano učenje je jedna kategorija strojnog učenja, uz nadzirano i nenadzirano učenje, te je ono najprikladnije za primjenu kod igara. U podržanom učenju, agent promatra svoju okolinu te uči donositi odluke o akcijama koje treba napraviti kako bi maksimizirao nagradu. Proces na kojem je bazirano podržano učenje naziva se Markovljev proces odlučivanja. U ovom radu korištena je metoda dubokog Q učenja koja kombinira duboko učenje te Q učenje tako da neuronska mreža zamjenjuje Q tablicu. U ovom radu principi podržanog učenja metodom dubokog Q učenja prikazani su na primjeru agenta koji uči igrati igricu „Snake“. Za izradu neuronske mreže korištena je biblioteka Keras.
Abstract (english)
Reinforcement learning is a category of machine learning, along with supervised and unsupervised learning and it is most suitable for application in games. In reinforcement learning, agent observeres it's environment and learns to make decisions about taking actions to maximize it's reward. Process on which reinforcement learning is based is called Markov Decision Process. Method used in this paper is deep Q learning which combines deep learning and Q learning by replacing Q table with neural network. In this paper, principles of reinforcement learning with deep Q learning method is demonstrated on agent that learns to play game Snake. Library that is used for making neural networks is Keras.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:769026
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
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File originBorn digital
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Created on2020-01-07 21:38:58