Using a reinforcement Q-learning-based deep neural network for playing video games

Cheng Jian Lin*, Jyun Yu Jhang, Chin Ling Lee, Hsueh Yi Lin, Kuu Young Young

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


This study proposed a reinforcement Q-learning-based deep neural network (RQDNN) that combined a deep principal component analysis network (DPCANet) and Q-learning to determine a playing strategy for video games. Video game images were used as the inputs. The proposed DPCANet was used to initialize the parameters of the convolution kernel and capture the image features automatically. It performs as a deep neural network and requires less computational complexity than traditional convolution neural networks. A reinforcement Q-learning method was used to implement a strategy for playing the video game. Both Flappy Bird and Atari Breakout games were implemented to verify the proposed method in this study. Experimental results showed that the scores of our proposed RQDNN were better than those of human players and other methods. In addition, the training time of the proposed RQDNN was also far less than other methods.

Original languageEnglish
Article number1128
JournalElectronics (Switzerland)
Issue number10
StatePublished - Oct 2019


  • Convolution neural network
  • Deep principal component analysis network
  • Image sensor
  • Q-learning
  • Reinforcement learning
  • Video game


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