COMPACT FUZZY Q LEARNING FOR AUTONOMOUS MOBILE ROBOT NAVIGATION

dc.contributor.author	Wicaksono, Handy
dc.contributor.author	Anam, Khairul
dc.contributor.author	Prihastono, Prihastono
dc.contributor.author	Sulistijono, Indra Adjie
dc.contributor.author	Kuswadi, Son
dc.date.accessioned	2017-10-11T07:49:32Z
dc.date.available	2017-10-11T07:49:32Z
dc.date.issued	2017-10-11
dc.identifier.isbn	978-0-88986-858-8
dc.identifier.uri	http://repository.unej.ac.id/handle/123456789/82064
dc.description	Prosiding IASTED International Conference on Robotics	en_US
dc.description.abstract	Robot which does complex task needs learning capability. Q learning is popular reinforcement learning method because it has off-line policy characteristic and simple algorithm. But it only suitable in discrete state and action. By using Fuzzy Q Learning (FQL), continuous state and action can be handled too. Unfortunately, it’s not easy to implement FQL algorithm to real robot because its complexity and robot’s limited memory capacity. In this research, Compact FQL (CFQL) algorithm is proposed to solve those weaknesses. By using CFQL, robot still can accomplish its task in autonomous navigation although its performance is not as good as robot using FQL.	en_US
dc.language.iso	en	en_US
dc.subject	Autonomous robot	en_US
dc.subject	fuzzy Q learning	en_US
dc.subject	Navigation	en_US
dc.title	COMPACT FUZZY Q LEARNING FOR AUTONOMOUS MOBILE ROBOT NAVIGATION	en_US
dc.type	Prosiding	en_US