Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa

Reitinhaku on yksi suurimmista ongelmista tekoälyn tutkimuksessa. Viime vuosikymmenten aikana sekä robotiikan että videopelien reitinhakuongelmat ovat tuottaneet erilaisia ratkaisuja kuten A*-algoritmi ja sen variaatiot. Videopeleissä etenkin A*-algoritmia on pidetty luotettavana ratkaisuna sen opti...

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Main Author: Keränen, Emil
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:fin
Published: 2022
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/84540
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author Keränen, Emil
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Keränen, Emil Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Keränen, Emil Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Keränen, Emil
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description Reitinhaku on yksi suurimmista ongelmista tekoälyn tutkimuksessa. Viime vuosikymmenten aikana sekä robotiikan että videopelien reitinhakuongelmat ovat tuottaneet erilaisia ratkaisuja kuten A*-algoritmi ja sen variaatiot. Videopeleissä etenkin A*-algoritmia on pidetty luotettavana ratkaisuna sen optimaalisuuden takia. Dynaamiset pelialueet ja moniagenttireitinhaku ovat kuitenkin tuoneet haasteita, joihin A*-algoritmi ei ole pystynyt yksin vastaamaan. Tässä tutkimuksessa hyödynnetään Unity-pelinkehitysalustalle luotua ML-agents-pakettia koneoppimisagentin luomiseen ja testataan sen soveltuvuutta reitinhakutehtäviin. Koneoppimisagentit käyttävät syvää vahvistusoppimista ja siihen perustuvaa Soft Actor-Critic -algoritmia. Lopuksi tarkoituksena on vertailla perinteisen A*-algoritmin tuloksia koneoppimisagentin tuloksiin. Pathfinding or path planning is one of the major problems in AI research. During the last decades pathfinding in both robotics and video games has produced different solutions like A*-algorithm and its variations. In video games especially A*-algorithm has been the reliable solution because of its optimality. Dynamic video game environments and multi-agent pathfinding have brought challenges where A*-algorithm alone has proven to be insufficient. In this thesis Unity platform and its ML-agents-package will be used to create machine learning agent for executing pathfinding tasks. Machine learning agent uses deep reinforcement learning and Soft Actor-Critic -algorithm. Lastly A*-based solutions and machine learning agents will be compared against each other.
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spellingShingle Keränen, Emil Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa reitinhaku Tietotekniikka Mathematical Information Technology 602 tekoäly koneoppiminen videopelit pelit
title Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa
title_full Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa
title_fullStr Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa
title_full_unstemmed Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa
title_short Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa
title_sort vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa
title_txtP Vertaileva tutkimus koneoppimisen hyödyntämisestä videopelien reitinhaussa
topic reitinhaku Tietotekniikka Mathematical Information Technology 602 tekoäly koneoppiminen videopelit pelit
topic_facet 602 Mathematical Information Technology Tietotekniikka koneoppiminen pelit reitinhaku tekoäly videopelit
url https://jyx.jyu.fi/handle/123456789/84540 http://www.urn.fi/URN:NBN:fi:jyu-202212215786
work_keys_str_mv AT keränenemil vertailevatutkimuskoneoppimisenhyödyntämisestävideopelienreitinhaussa