Learning Competitive Minecraft Minigames with Reinforcement Learning

dc.contributor.advisorMatiisen, Tambet, juhendaja
dc.contributor.authorSisask, Laur
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Arvutiteaduse instituutet
dc.date.accessioned2023-08-17T13:15:41Z
dc.date.available2023-08-17T13:15:41Z
dc.date.issued2022
dc.description.abstractIn 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.urihttps://hdl.handle.net/10062/91638
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectreinforcement learninget
dc.subjectartificial neural networkset
dc.subjectself-playet
dc.subject.otherbakalaureusetöödet
dc.subject.otherinformaatikaet
dc.subject.otherinfotehnoloogiaet
dc.subject.otherinformaticset
dc.subject.otherinfotechnologyet
dc.titleLearning Competitive Minecraft Minigames with Reinforcement Learninget
dc.typeThesiset

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Laur_Sisask_thesis.pdf
Size:
9.98 MB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: