Deep Learning and the Game of Checkers

dc.contributor.authorPopic, Jan
dc.contributor.authorBoskovic, Borko
dc.contributor.authorBrest, Janez
dc.coverage.issue2cs
dc.coverage.volume27cs
dc.date.accessioned2022-01-26T08:21:41Z
dc.date.available2022-01-26T08:21:41Z
dc.date.issued2021-12-21cs
dc.description.abstractIn this paper we present an approach which given only a set of rules is able to learn to play the game of Checkers. We utilize neural networks and reinforced learning combined with Monte Carlo Tree Search and alpha-beta pruning. Any human influence or knowledge is removed by generating needed data, for training neural network, using self-play. After a certain number of finished games, we initialize the training and transfer better neural network version to next iteration. We compare different obtained versions of neural networks and their progress in playing the game of Checkers. Every new version of neural network represented a better player.en
dc.formattextcs
dc.format.extent1-6cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2021 vol. 27, č. 2, s. 1-6. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2021.2.001en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/203381
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/163cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectArtificial Intelligenceen
dc.subjectDeep Learningen
dc.subjectConvolutional Neural Networken
dc.subjectReinforcement Learningen
dc.subjectCheckersen
dc.titleDeep Learning and the Game of Checkersen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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