A Comparative Analysis of Reinforcement Learning Methods

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5978

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DC Field	Value	Language
dc.creator	Mataric, Maja	-
dc.date	2004-10-04T14:25:16Z	-
dc.date	2004-10-04T14:25:16Z	-
dc.date	1991-10-01	-
dc.date.accessioned	2013-10-09T02:42:10Z	-
dc.date.available	2013-10-09T02:42:10Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1322	-
dc.identifier	http://hdl.handle.net/1721.1/5978	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapting situated agents. We discuss two RL algorithms: Q-learning and the Bucket Brigade. We introduce a special case of the Bucket Brigade, and analyze and compare its performance to Q in a number of experiments. Next we discuss the key problems of RL: time and space complexity, input generalization, sensitivity to parameter values, and selection of the reinforcement function. We address the tradeoffs between the built-in and learned knowledge and the number of training examples required by a learning algorithm. Finally, we suggest directions for future research.	-
dc.format	13 p.	-
dc.format	1444645 bytes	-
dc.format	1130480 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1322	-
dc.subject	reinforcement	-
dc.subject	learning	-
dc.subject	situated agents	-
dc.subject	inputsgeneralization	-
dc.subject	complexity	-
dc.subject	built-in knowledge	-
dc.title	A Comparative Analysis of Reinforcement Learning Methods	-
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