Performing Deep Recurrent Double Q-Learning for Atari Games

• Autor: Moreno-Vera F.
• Formato: artículo
• Fecha de Publicación: 2019
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/2687
• Enlace del recurso: https://hdl.handle.net/20.500.12390/2687; https://doi.org/10.1109/LA-CCI47412.2019.9036763
• Nivel de acceso: acceso abierto
• Materia: Reinforcement Learning; Atari Games; DDQN; Deep Reinforcement Learning; Double Q-Learning; DQN; DRQN; Recurrent Q-Learning; http://purl.org/pe-repo/ocde/ford#2.02.04

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.