Performing Deep Recurrent Double Q-Learning for Atari Games

Felipe Moreno-Vera (1, 2)

1 UNI - Universidad Nacional de Ingeniería
2 UCSP - Universidad Católica San Pablo

Currently, many applications in Machine Learning are based on define 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 which is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.

Mots clés: Convolutional Networks; Double Q-Learning; Reinforcement Learning; Recurrent Networks; Atari; Video Games

Domaines: Méthodes et statistiques; Informatique [cs]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Felipe Moreno-Vera. Performing Deep Recurrent Double Q-Learning for Atari Games. International Conference on Machine Learning, LatinX in AI Workshop, Jun 2019, Long Beach, United States. ⟨hal-02217800v1⟩