Learning Competitive Minecraft Minigames with Reinforcement Learning
| dc.contributor.advisor | Matiisen, Tambet, juhendaja | |
| dc.contributor.author | Sisask, Laur | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Arvutiteaduse instituut | et |
| dc.date.accessioned | 2023-08-17T13:15:41Z | |
| dc.date.available | 2023-08-17T13:15:41Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In recent years, deep reinforcement learning methods have successfully been used to play complex games like Go, StarCraft II, and Dota 2 at a professional level. In this thesis, reinforcement learning methods are used to train artificial agents in the game of Minecraft. Various competitive 1v1 Minecraft minigames from one of the most popular Minecraft servers Hypixel are selected. Deep neural networks are trained to play each of these games using proximal policy optimization algorithms and self-play. In all the games, artificial agents were able to play the game at least on a beginner level. In one game, the agent reached the level of expert human players. | et |
| dc.identifier.uri | https://hdl.handle.net/10062/91638 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | reinforcement learning | et |
| dc.subject | artificial neural networks | et |
| dc.subject | self-play | et |
| dc.subject.other | bakalaureusetööd | et |
| dc.subject.other | informaatika | et |
| dc.subject.other | infotehnoloogia | et |
| dc.subject.other | informatics | et |
| dc.subject.other | infotechnology | et |
| dc.title | Learning Competitive Minecraft Minigames with Reinforcement Learning | et |
| dc.type | Thesis | et |