Globoko učenje in igra dama : diplomsko delo

Popič, Jan

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Title:	Globoko učenje in igra dama : diplomsko delo
Authors:	ID Popič, Jan (Author) ID Bošković, Borko (Mentor) More about this mentor... ID Brest, Janez (Comentor)
Files:	UN_Popic_Jan_2019.pdf (749,47 KB) MD5: 38860675CCA1655B56B182534EEE84C2 PID: 20.500.12556/dkum/a2d8d1ad-1047-421a-9df0-25dee88b39bb
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V zaključnem delu smo zasnovali računalniški program AlphaLady, ki se je sposoben naučiti igranja igre dama brez vnosa človeškega znanja. Za dosego tega smo uporabili vzpodbujevalno učenje, drevesno preiskovanje Monte Carlo in globoke konvolucijske mreže za ocenitev posameznih stanj v igri. Predstavili smo programe Alpha Go, AlphaGo Zero in AlphaZero, na podlagi katerih je zasnovan naš program. Opisali smo uporabljeno ogrodje in teoretično ozadje uporabljenih pristopov. Uspelo nam je naučiti 9 različic programa, pri čemer je vsaka naslednja različica enakovredna ali boljša kot prejšnja.
Keywords:	umetna inteligenca, globoko učenje, konvolucijska nevronska mreža, drevesno preiskovanje Monte Carlo, vzpodbujevalno učenje, igra dama
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[J. Popič]
Year of publishing:	2019
Number of pages:	XVII, 50 str.
PID:	20.500.12556/DKUM-74323
UDC:	004.8(043.2)
COBISS.SI-ID:	22848534
NUK URN:	URN:SI:UM:DK:LMRHYGF1
Publication date in DKUM:	13.11.2019
Views:	2048
Downloads:	244
Metadata:
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:	23.08.2019

Secondary language

Language:	English
Title:	Deep Learning and the game of Checkers
Abstract:	In the thesis, we designed a computer program AlphaLady, which is capable of learning to play the game of checkers without human knowledge. To achieve this we have used reinforcement learning, Monte Carlo tree search and deep convolutional neural network for evaluating board positions. Our program is based on the introduced programs Alpha Go, AlphaGo Zero and AlphaZero. We described a framework that was used for implementation and theoretical background of used approaches. We managed to train 9 versions of our program, with each successive version being equal or better than the previous one.
Keywords:	artificial intelligence, deep learning, convolutional neural network, Monte Carlo tree search, reinforcement learning, checkers

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