A reinforcement learning approach based on the fuzzy min-max neural network

Likas, A; Blekas, K

dc.contributor.author	Likas, A	en
dc.contributor.author	Blekas, K	en
dc.date.accessioned	2014-03-01T01:11:37Z
dc.date.available	2014-03-01T01:11:37Z
dc.date.issued	1996	en
dc.identifier.issn	1370-4621	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11744
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0030349546&partnerID=40&md5=c05164ad874cfa914181680c1b8599f6	en
dc.subject	Autonomous vehicle navigation	en
dc.subject	Fuzzy min-max neural network	en
dc.subject	Reinforcement learning	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Neurosciences	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Navigation	en
dc.subject.other	Neural networks	en
dc.subject.other	Robots	en
dc.subject.other	Fuzzy min max neural networks	en
dc.subject.other	Reinforcement learning	en
dc.subject.other	Learning systems	en
dc.title	A reinforcement learning approach based on the fuzzy min-max neural network	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	1996	en
heal.abstract	The 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. © 1996 Kluwer Academic Publishers.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	Neural Processing Letters	en
dc.identifier.isi	ISI:A1996WN61100006	en
dc.identifier.volume	4	en
dc.identifier.issue	3	en
dc.identifier.spage	167	en
dc.identifier.epage	172	en