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Reinforcement learning in Tarock
ID Požrl, Domen (Author), ID Sadikov, Aleksander (Mentor) More about this mentor... This link opens in a new window

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Abstract
In this thesis we study and analyse the effectiveness of Q-learning when it comes to learning and playing the card game Tarock. We present many problems that are inherently present in every attempt at mastering a card game with artificial intelligence. We developed several models, each with a distinct way of looking at the game and distinct behaviour in different game scenarios. We designed different testing conditions ranging from playing against theoretical players and other Tarock playing software, to playing against real people. We evaluated our models under real life and computational conditions. Our best performing model was able to compete with, and on many occasions beat an average Tarock player.

Language:English
Keywords:reinforcement learning, Tarock, cards, game playing
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FRI - Faculty of Computer and Information Science
Year:2021
PID:20.500.12556/RUL-133070 This link opens in a new window
COBISS.SI-ID:87302915 This link opens in a new window
Publication date in RUL:10.11.2021
Views:1205
Downloads:125
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Secondary language

Language:Slovenian
Title:Spodbujevalno učenje v taroku
Abstract:
V tem magistrskem delu preučimo in analiziramo učinkovitost metode Q-learning v kontekstu učenja in igranja igre tarok. Predstavimo probleme, ki se pojavljajo pri vsakem poskusu igranja iger s kartami s pomočjo umetne inteligence. Razvili smo več modelov, vsakega z drugačnim pogledom na igro in drugačnim odgovorom na različna stanja igre. Zasnovali smo več testnih okolij, od igranja proti teoretičnim igralcem in drugim programom za igranje taroka, do igranja proti pravim ljudem. Modele smo ovrednotili pod življenjskimi in računskimi pogoji. Naš najboljši model se je uspešno boril in večkrat celo premagoval povprečne igralce taroka.

Keywords:spodbujevalno učenje, tarok, karte, igranje iger

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