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Title:Strojno učenje računalniškega igralca v igri s kartami : diplomsko delo
Authors:ID Praskalo, Mai (Author)
ID Strnad, Damjan (Mentor) More about this mentor... New window
ID Kohek, Štefan (Comentor)
Files:.pdf UN_Praskalo_Mai_2021.pdf (901,04 KB)
MD5: 2073105FB2A8C8CCCD56974F42020335
PID: 20.500.12556/dkum/5762abbc-01ed-4811-84d7-31e8d2cc4e89
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomski nalogi predstavljamo več različnih implementacij strojnega učenja računalniškega igralca za igranje igre s kartami Uno. Vsi uporabljeni algoritmi so s področja okrepitvenega učenja, saj so klasični algoritmi, ki se zanašajo na iskanje optimalne poteze na podlagi popolne informacije, neprimerni za igre z nepopolnimi informacijami. Algoritme smo primerjali glede na uspešnost v igranju proti igralcu, ki izbira naključne poteze, ter glede na krivuljo učenja, ki prikazuje pridobljeno povprečno kumulativno nagrado med procesom učenja.
Keywords:okrepitveno učenje, igra Uno, igre z nepopolnimi informacijami, igre s kartami, nevronske mreže
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[M. Praskalo]
Year of publishing:2021
Number of pages:IX, 31 str.
PID:20.500.12556/DKUM-80359 New window
UDC:004.85(043.2)
COBISS.SI-ID:92402947 New window
Publication date in DKUM:18.10.2021
Views:900
Downloads:33
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:09.09.2021

Secondary language

Language:English
Title:Machine learning of a computer player in a card game
Abstract:In this graduate thesis we present several different implementations of machine learning of a computer player for playing the Uno card game. All used algorithms are from the field of reinforcement learning, as classic algorithms that rely on finding optimal moves based on complete information are unsuitable for games with incomplete information. We compared the algorithms according to their performance when playing against a player that chooses random actions, and according to the learning curve, which represents the obtained cumulative reward during the learning process.
Keywords:reinforcement learning, card game Uno, imperfect information games, card games, neural networks


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