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Mastering the Game of Abalone using Deep Reinforcement Learning and Self-Play

  • AlphaGo’s victory against Lee Sedol in the game of Go has been a milestone in artificial intelligence. After this success, the team behind the program further refined the architecture and applied it to many other games such as chess or shogi. In the following thesis, we try to apply the theory behind AlphaGo and its successor AlphaZero to the game of Abalone. Due to limitations in computational resources, we could not replicate the same exceptional performance.

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Author:Ture Claußen
URN:urn:nbn:de:bsz:960-opus4-22780
DOI:https://doi.org/10.25968/opus-2278
Advisor:Adrian PigorsGND, Ralf BrunsORCiDGND
Document Type:Bachelor Thesis
Language:English
Year of Completion:2022
Publishing Institution:Hochschule Hannover
Granting Institution:Hochschule Hannover, Fakultät IV - Wirtschaft und Informatik
Date of final exam:2022/02/23
Release Date:2022/05/24
Tag:AI; Abalone; AlphaGo; Machine Learning; Reinforcement Learning
GND Keyword:Bestärkendes Lernen <Künstliche Intelligenz>; Künstliche Intelligenz; Maschinelles Lernen; Brettspiel
Page Number:86
Note:
Begleitmaterial ist unter https://github.com/campfireman/bachelor-thesis abrufbar.

Supplementary material is available at https://github.com/campfireman/bachelor-thesis.
Institutes:Fakultät IV - Wirtschaft und Informatik
DDC classes:004 Informatik
Licence (German):License LogoCreative Commons - CC0 1.0 - Universell - Public Domain Dedication