Exploration in Gradient-Based Reinforcement Learning

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6076

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DC Field	Value	Language
dc.creator	Meuleau, Nicolas	-
dc.creator	Peshkin, Leonid	-
dc.creator	Kim, Kee-Eung	-
dc.date	2004-10-04T14:37:39Z	-
dc.date	2004-10-04T14:37:39Z	-
dc.date	2001-04-03	-
dc.date.accessioned	2013-10-09T02:42:44Z	-
dc.date.available	2013-10-09T02:42:44Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-2001-003	-
dc.identifier	http://hdl.handle.net/1721.1/6076	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Gradient-based policy search is an alternative to value-function-based methods for reinforcement learning in non-Markovian domains. One apparent drawback of policy search is its requirement that all actions be 'on-policy'; that is, that there be no explicit exploration. In this paper, we provide a method for using importance sampling to allow any well-behaved directed exploration policy during learning. We show both theoretically and experimentally that using this method can achieve dramatic performance improvements.	-
dc.format	5594043 bytes	-
dc.format	516972 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-2001-003	-
dc.title	Exploration in Gradient-Based Reinforcement Learning	-
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