Strojno učenje računalniškega igralca v igri Havannah : diplomsko delo

Serec, Nino

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Title:	Strojno učenje računalniškega igralca v igri Havannah : diplomsko delo
Authors:	ID Serec, Nino (Author) ID Strnad, Damjan (Mentor) More about this mentor...
Files:	UN_Serec_Nino_2020.pdf (1,29 MB) MD5: 42F2B3031EB6FB090511A641560D4F62 PID: 20.500.12556/dkum/7fe92ae5-9424-4c6b-a043-81cad234730a
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V zadnjih letih je bil na področju umetne inteligence z uporabo okrepitvenega učenja nevronskih mrež dosežen preboj pri sposobnostih računalnika za igranje iger na deski, kot je Go, pri katerih je bil človek doslej močnejši nasprotnik. V diplomskem delu raziščemo algoritem igranja iger AlphaZero, ki kombinira tehnike preiskovanja dreves Monte Carlo in okrepitvenega učenja nevronskih mrež. Algoritem začne brez posebnega predznanja o dobrih strategijah, vendar se moč algoritma s postopkom učenja, ki se ponavlja iterativno, konstantno povečuje. V diplomskem delu opišemo in implementiramo osnovno obliko AlphaZero za igranje igre Havannah. Naučimo več različic modela nevronskih mrež, kjer vsak naslednik premaga svojega prednika in postane prvak. S tem pokažemo, da se lahko računalniški igralec uči igranja igre Havannah samo s podanimi pravili igre, tako da je sposoben premagati povprečnega človeškega igralca.
Keywords:	igra Havannah, drevesno preiskovanje Monte Carlo, nevronske mreže, okrepitveno učenje, tabula rasa
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[N. Serec]
Year of publishing:	2020
Number of pages:	VIII, 42 f.
PID:	20.500.12556/DKUM-78061
UDC:	004.388.4:004.85(043.2)
COBISS.SI-ID:	45050627
NUK URN:	URN:SI:UM:DK:HGVJIGUO
Publication date in DKUM:	11.11.2020
Views:	1362
Downloads:	100
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International

Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:	14.10.2020

Secondary language

Language:	English
Title:	Machine learning of computer player in Havannah game
Abstract:	In recent years, in the field of artificial intelligence, the reinforcement learning of neural networks has been used to achieve a breakthrough in the ability of the computer players to play board games, such as Go, in which human has been a stronger opponent. In this thesis, we explore the AlphaZero algorithm, which combines Monte Carlo tree search and reinforced neural network learning. The algorithm starts without any special prior knowledge of good strategies, but the algorithm becomes stronger with a learning process that repeats iteratively. In this thesis, we implement the basic form of AlphaZero for playing the Havannah game. Several versions of the neural network model are trained to play the game, where each successor defeats its predecessor and becomes the champion, thus showing that a computer player can learn to play the Havannah game and win against a human player, simply by being given the rules of the game and not possessing any special prior knowledge of good strategies.
Keywords:	Havannah, Monte Carlo tree search, neural networks, reinforced learning, tabula rasa

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