Thesis Details

Hluboké neuronové sítě pro posilované učení v realtimové strategii

Bachelor's Thesis Student: Barilla Marco Academic Year: 2018/2019 Supervisor: Kolář Martin, Ph.D.
English title
Deep Neural Networks for Reinforcement Learning in Real-Time Strategy
Language
Czech
Abstract

Machine learning is one of the fastest growing branches of modern science. It is a subfield of artificial intelligence research that is interested the problem of making computers help us solve complex modern problems. Games play an important role in this field because they represent the perfect environment for testing of new approaches and benchmarking against human performance. Starcraft 2 is currently in the spotlight, thanks to its broad playerbase and its complexity. The practical goal of this paper is to create an advantage actor critic agent that is able to operate in the environment of this game.

Keywords

machine learning, reinforcement learning, deep neural networks, A2C, Starcraft 2, pysc2

Department
Department of Computer Graphics and Multimedia FIT BUT
Degree Programme
Information Technology
Files
Status
defended, grade D
Date
13 June 2019
Reviewer
Dobeš Petr, Ing.
Committee
Zbořil František, doc. Ing., Ph.D. (DITS FIT BUT), předseda
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Grézl František, Ing., Ph.D. (DCGM FIT BUT), člen
Hliněná Dana, doc. RNDr., Ph.D. (DMAT FEEC BUT), člen
Strnadel Josef, Ing., Ph.D. (DCSY FIT BUT), člen
Citation
BARILLA, Marco. Hluboké neuronové sítě pro posilované učení v realtimové strategii. Brno, 2019. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2019-06-13. Supervised by Kolář Martin. Available from: https://www.fit.vut.cz/study/thesis/22123/
BibTeX 
@bachelorsthesis{FITBT22123,
    author = "Marco Barilla",
    type = "Bachelor's thesis",
    title = "Hlubok\'{e} neuronov\'{e} s\'{i}t\v{e} pro posilovan\'{e} u\v{c}en\'{i} v realtimov\'{e} strategii",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2019,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/22123/"
}

Theses

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