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.
Author: | Ture Claußen |
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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. |
Link to catalogue: | 1809368480 |
Institutes: | Fakultät IV - Wirtschaft und Informatik |
DDC classes: | 004 Informatik |
Licence (German): | Creative Commons - CC0 1.0 - Universell - Public Domain Dedication |