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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11095
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dc.contributor.authorLikas, A.en
dc.contributor.authorBlekas, K.en
dc.date.accessioned2015-11-24T17:02:48Z-
dc.date.available2015-11-24T17:02:48Z-
dc.identifier.issn1370-4621-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11095-
dc.rightsDefault Licence-
dc.subjectfuzzy min-max neural networken
dc.subjectreinforcement learningen
dc.subjectautonomous vehicle navigationen
dc.titleA reinforcement learning approach based on the fuzzy min-max neural networken
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate1996-
heal.abstractThe fuzzy min-max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding volumes. Two versions have been proposed: for supervised and unsupervised learning. In this paper a modified approach is presented that is appropriate for reinforcement learning problems with discrete action space and is applied to the difficult task of autonomous vehicle navigation when no a priori knowledge of the enivronment is available. Experimental results indicate that the proposed reinforcement learning network exhibits superior learning behavior compared to conventional reinforcement schemes.en
heal.journalNameNeural Processing Lettersen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
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