Autonomous vehicle navigation using evolutionary reinforcement learning

dc.contributor.authorStafylopatis, A.en
dc.contributor.authorBlekas, K.en
dc.date.accessioned2015-11-24T17:03:04Z
dc.date.available2015-11-24T17:03:04Z
dc.identifier.issn0377-2217-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11127
dc.rightsDefault Licence-
dc.subjectLearning classifier systemsen
dc.subjectGenetic algorithmsen
dc.subjectReinforcement learningen
dc.subjectAutonomous navigationen
dc.titleAutonomous vehicle navigation using evolutionary reinforcement learningen
heal.abstractReinforcement learning schemes perform direct on-line search in control space. This makes them appropriate for modifying control rules to obtain improvements in the performance of a system. The effectiveness of a reinforcement learning strategy is studied here through the training of a learning classifier system (LCS) that controls the movement of an autonomous vehicle in simulated paths including left and right turns. The LCS comprises a set of condition-action rules (classifiers) that compete to control the system and evolve by means of a genetic algorithm (GA). Evolution and operation of classifiers depend upon an appropriate credit assignment mechanism based on reinforcement learning. Different design options and the role of various parameters have been investigated experimentally. The performance of vehicle movement under the proposed evolutionary approach is superior compared with that of other (neural) approaches based on reinforcement learning that have been applied previously to the same benchmark problem.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.primary10.1016/s0377-2217(97)00372-x-
heal.journalNameEuropean Journal of Operational Researchen
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate1998-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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