VQQL: a model to generalize in reinforcement learning

Título

dc.contributor	Escuela de Ingenierias Industrial, Informática y Aeroespacial	es_ES
dc.contributor.author	Fernández, Fernando
dc.contributor.author	Borrajo, Daniel
dc.contributor.author	Matellán Olivera, Vicente
dc.contributor.other	Arquitectura y Tecnologia de Computadores	es_ES
dc.date	1999-09-10
dc.date.accessioned	2012-10-18T12:20:09Z
dc.date.available	2012-10-18T12:20:09Z
dc.date.issued	2012-10-18
dc.identifier.citation	European Conference on Planning, Septiembre, 1999, Durham, Reino Unido	es_ES
dc.identifier.uri	http://hdl.handle.net/10612/1921
dc.description.abstract	Reinforcement learning har proven to be very successful for finding optimal policies on uncertian and/or dynamic domains. One of the problems on using such techniques appears with large state and action spaces. This problem appears very frequently given that most information in the type of tasks to which these techniques have been applied is continuous. In the paper, we describe a new mechanism for solving the states generalization problem in reinforcement learning algorithms, the VQQL technique	es_ES
dc.language	eng	es_ES
dc.subject	Informática	es_ES
dc.subject.other	Conocimiento	es_ES
dc.subject.other	Robótica	es_ES
dc.subject.other	VQQL	es_ES
dc.subject.other	Aprendizaje	es_ES
dc.title	VQQL: a model to generalize in reinforcement learning	es_ES
dc.type	info:eu-repo/semantics/conferenceObject	es_ES
dc.type.other	info:eu-repo/semantics/lecture	es_ES
dc.rights.accessRights	info:eu-repo/semantics/openAccess	es_ES