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

No hay miniatura disponible
Fecha
2019
Autores
Camara G.
Lopez J.
Mauricio A.
Título de la revista
Revista ISSN
Título del volumen
Editor
Institute of Electrical and Electronics Engineers Inc.
Proyectos de investigación
Unidades organizativas
Número de la revista
Abstracto
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.
Descripción
Palabras clave
Reinforcement Learning, Atari Games, DDQN, Deep Reinforcement Learning, Double Q-Learning, DQN, DRQN, Recurrent Q-Learning
Citación