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dc.contributor.advisorTerziyan, Vagan
dc.contributor.authorTsybulko, Vitalii
dc.date.accessioned2019-05-29T06:28:43Z
dc.date.available2019-05-29T06:28:43Z
dc.date.issued2019
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/64268
dc.description.abstractOne problem of current Reinforcement Learning algorithms is finding a balance between exploitation of existing knowledge and exploration for a new experience. Curiosity exploration bonus has been proposed to address this problem, but current implementations are vulnerable to stochastic noise inside the environment. The new approach presented in this thesis utilises exploration bonus based on the predicted novelty of the next state. That protects exploration from noise issues during training. This work also introduces a new way of combining extrinsic and intrinsic rewards. Both improvements help to overcome a number of problems that Reinforcement Learning had until now.en
dc.format.extent63
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subject.otherreinforcement learning
dc.subject.otherproximal policy optimisation
dc.subject.othercuriosity-driven exploration bonus
dc.titleCuriosity-driven algorithm for reinforcement learning
dc.identifier.urnURN:NBN:fi:jyu-201905292863
dc.type.ontasotPro gradu -tutkielmafi
dc.type.ontasotMaster’s thesisen
dc.contributor.tiedekuntaInformaatioteknologian tiedekuntafi
dc.contributor.tiedekuntaFaculty of Information Technologyen
dc.contributor.laitosInformaatioteknologiafi
dc.contributor.laitosInformation Technologyen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.rights.copyrightJulkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.fi
dc.rights.copyrightThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.en
dc.type.publicationmasterThesis
dc.contributor.oppiainekoodi602
dc.subject.ysotekoäly
dc.subject.ysokoneoppiminen
dc.subject.ysopalkitseminen
dc.subject.ysoartificial intelligence
dc.subject.ysomachine learning
dc.subject.ysorewarding
dc.format.contentfulltext
dc.type.okmG2


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