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Uporaba genetskih algoritmov za učenje inteligentnih agentov v računalniških igrah
ID Bevc, Jure (Author), ID Lebar Bajec, Iztok (Mentor) More about this mentor... This link opens in a new window, ID Demšar, Jure (Comentor)

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Abstract
Uporaba strojnega učenja v računalniških igrah postaja vse bolj pogosta za razvoj vedenja inteligentih agentov. Najpogostejši pristop k problemu je uporaba spodbujevanega učenja, ki se je že večkrat izkazalo za učinkovito pri iskanju robustnih rešitev. V diplomski nalogi smo, kot alternativno rešitev, uporabili genetske algoritme, ki so kljub njihovi enostavnosti le redko uporabljeni za razvoj vedenja inteligentnih agentov. Učinkovitost implementacije smo primerjali s splošno razširjeno rešitvijo ML-Agents, ki je osnovana na spodbujevanem učenju. Primerjava med pristopoma je bila izvedena na dveh popularnih igrah, pod primerljivimi pogoji. Naši rezultati nakazujejo, da je uporaba genetskih algoritmov smiselna za enostavnejše scenarije, medtem ko se v bolj kompleksnih primerih, ko je za reševanje danega problema zahtevano kompleksnejše vedenje, naša rešitev ni obnesla najbolje.

Language:Slovenian
Keywords:genetski algoritmi, spodbujevano učenje, računalniške igre
Work type:Bachelor thesis/paper
Organization:FRI - Faculty of Computer and Information Science
Year:2019
PID:20.500.12556/RUL-109541 This link opens in a new window
COBISS.SI-ID:1538316483 This link opens in a new window
Publication date in RUL:05.09.2019
Views:1317
Downloads:215
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Secondary language

Language:English
Title:Use of genetic algorithms for development of intelligent agents in games
Abstract:
Machine learning techniques are already commonly applied for developing the behaviour of intelligent agents in video games. Most commonly the development of agents is executed via reinforced learning, a relatively simple approach, capable of producing robust solutions to various learning challenges. In the presented thesis we tested whether genetic algorithms could be a viable alternative to reinforced learning. Even though genetic algorithms are very simple and easy to implement they have not seen much use when it comes to development of intelligent agents. To compare the quality of our genetic algorithms based solution, we compared it with ML-Agents, a widespread framework for development of intelligent agents, based on reinforced learning. The comparison of both learning methods. was executed on two popular games under comparable conditions. Our results suggest that genetic algorithms could represent a viable alternative to reinforced learning, but only in simple scenarios. When applied to more complex scenarios, our implementation of genetic results did not fare extremely well.

Keywords:genetic algorithms, reinforcement learning, computer games

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