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dc.contributor.authorLikas, A.en
dc.contributor.authorBlekas, K.en
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.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
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-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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