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Title:Uporaba okrepitvenega učenja pri učenju igralnih agentov v borilnih igrah : magistrsko delo
Authors:ID Milošič, Tomi (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Novak, Damijan (Comentor)
Files:.pdf MAG_Milosic_Tomi_2023.pdf (1,85 MB)
MD5: BF33C61D34B1FA9C02C0F2FB28706BD1
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V tej magistrski nalogi smo se osredotočili na razvoj igralnega agenta z uporabo okrepitvenega učenja in posebej obravnavali vprašanje, ali so ti agenti dosegli ali presegli raven profesionalnih človeških igralcev. Delo je razdeljeno na teoretični in praktični del. Teoretični del zajema osnovne koncepte okrepitvenega učenja in opisuje uporabljena orodja. V praktičnem delu smo izvedli tri eksperimente z različnimi velikostmi korakov in časi usposabljanja modela ter nato primerjali njihovo uspešnost. Naša analiza kaže, da se v našem konkretnem primeru s povečevanjem časa učenja pri uporabi tehnik okrepitvenega učenja postopoma približujemo optimalnim rezultatom. Naša raziskava potrjuje učinkovitost okrepitvenega učenja pri oblikovanju igralnih agentov, katerih primerjava je relevantna zgolj ob zadostnem obsegu usposabljanja.
Keywords:Okrepitveno učenje, igralni lik, borilne igre, Gym Retro, PPO
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[T. Milošič]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (XIII ,61 f.))
PID:20.500.12556/DKUM-85528 New window
UDC:004.85:004.96(043.2)
COBISS.SI-ID:174509571 New window
Publication date in DKUM:12.10.2023
Views:357
Downloads:110
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
MILOŠIČ, Tomi, 2023, Uporaba okrepitvenega učenja pri učenju igralnih agentov v borilnih igrah : magistrsko delo [online]. Master’s thesis. Maribor : T. Milošič. [Accessed 23 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=85528
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:01.09.2023

Secondary language

Language:English
Title:Use of reinforcement learning for training game agents in fighting games
Abstract:In this master's thesis, we focused on developing a game agent using reinforcement learning, specifically addressing whether these agents have reached or surpassed the level of professional human players. The work is divided into theoretical and practical sections. The theoretical part covers fundamental reinforcement learning concepts and describes the tools used. In the practical section, we conducted three experiments with varying step sizes and training times for the model, subsequently comparing their performance. Our findings indicate that reinforcement learning techniques yield optimal results only when the agent is trained sufficiently long. In conclusion, our study supports the effectiveness of reinforcement learning in advancing game agents that can only be compared effectively after undergoing extensive training.
Keywords:Reinforcement learning, game character, fighting games, Gym Retro, PPO


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