Contribution to the study and the design of reinforcement functions

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dc.date.accessioned	2022-05-02T18:43:05Z
dc.date.available	2022-05-02T18:43:05Z
dc.date.issued	2000-06-26
dc.identifier.uri	http://sedici.unlp.edu.ar/handle/10915/135464
dc.description.abstract	The underlying concept in Reinforcement Learning is as simple as it is attractive: to learn by trial and error from the interaction with the environment. This approach allows us to deal with problems where a learning technique searches to improve the performance of the agent (the learner) over time. Reinforcement Learning groups a set of such techniques, and it uses a performance measure based on two types of signals given by a Critic or Reinforcement Function: penalty and reward.	en
dc.language	es	es
dc.subject	Reinforcement Learning	es
dc.subject	Artificial Neural Networks	es
dc.title	Contribution to the study and the design of reinforcement functions	en
dc.type	Articulo	es
sedici.identifier.uri	https://publicaciones.sadio.org.ar/index.php/EJS/article/view/127	es
sedici.identifier.issn	1514-6774	es
sedici.creator.person	Santos, Juan Miguel	es
sedici.subject.materias	Ciencias Informáticas	es
sedici.description.fulltext	true	es
mods.originInfo.place	Sociedad Argentina de Informática e Investigación Operativa	es
sedici.subtype	Articulo	es
sedici.rights.license	Creative Commons Attribution 4.0 International (CC BY 4.0)
sedici.rights.uri	http://creativecommons.org/licenses/by/4.0/
sedici.relation.journalTitle	Electronic Journal of SADIO	es
sedici.relation.journalVolumeAndIssue	vol. 3	es

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Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution 4.0 International (CC BY 4.0)