Publicación:
Performing Deep Recurrent Double Q-Learning for Atari Games

dc.contributor.author Moreno-Vera F. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2019
dc.description.abstract Currently, many applications in Machine Learning are based on defining new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, we proposed deep recurrent double Q-learning that is an improvement of the algorithms Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1109/LA-CCI47412.2019.9036763
dc.identifier.scopus 2-s2.0-85083110897
dc.identifier.uri https://hdl.handle.net/20.500.12390/2687
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof 2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
dc.subject Reinforcement Learning
dc.subject Atari Games es_PE
dc.subject DDQN es_PE
dc.subject Deep Reinforcement Learning es_PE
dc.subject Double Q-Learning es_PE
dc.subject DQN es_PE
dc.subject DRQN es_PE
dc.subject Recurrent Q-Learning es_PE
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#2.02.04
dc.title Performing Deep Recurrent Double Q-Learning for Atari Games
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
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oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
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