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Strojové učení ve strategických hrách / Machine Learning in Strategic Games

Machine learning is spearheading progress for the field of artificial intelligence in terms of providing competition in strategy games to a human opponent, be it in a game of chess, Go or poker. A field of machine learning, which shows the most promising results in playing strategy games, is reinforcement learning. The next milestone for the current research lies in a computer game Starcraft II, which outgrows the previous ones in terms of complexity, and represents a potential new breakthrough in this field. The paper focuses on analysis of the problem, and suggests a solution incorporating a reinforcement learning algorithm A2C and hyperparameter optimization implementation PBT, which could mean a step forward for the current progress.

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:385993
Date January 2018
CreatorsVlček, Michael
ContributorsŠkoda, Petr, Smrž, Pavel
PublisherVysoké učení technické v Brně. Fakulta informačních technologií
Source SetsCzech ETDs
LanguageCzech
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

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