Autonomous vehicle navigation using evolutionary reinforcement learning

Stafylopatis, A; Blekas, K

dc.contributor.author	Stafylopatis, A	en
dc.contributor.author	Blekas, K	en
dc.date.accessioned	2014-03-01T01:13:36Z
dc.date.available	2014-03-01T01:13:36Z
dc.date.issued	1998	en
dc.identifier.issn	0377-2217	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12600
dc.subject	Autonomous navigation	en
dc.subject	Genetic algorithms	en
dc.subject	Learning classifier systems	en
dc.subject	Reinforcement learning	en
dc.subject.classification	Management	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	ALGORITHMS	en
dc.title	Autonomous vehicle navigation using evolutionary reinforcement learning	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0377-2217(97)00372-X	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0377-2217(97)00372-X	en
heal.language	English	en
heal.publicationDate	1998	en
heal.abstract	Reinforcement 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. (C) 1998 Elsevier Science B.V.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	European Journal of Operational Research	en
dc.identifier.doi	10.1016/S0377-2217(97)00372-X	en
dc.identifier.isi	ISI:000074232900006	en
dc.identifier.volume	108	en
dc.identifier.issue	2	en
dc.identifier.spage	306	en
dc.identifier.epage	318	en