Next Article in Journal
An Energy and Area Efficient Carry Select Adder with Dual Carry Adder Cell
Previous Article in Journal
Development of a Hardware Simulator for Reliable Design of Modular Multilevel Converters Based on Junction-Temperature of IGBT Modules
Open AccessArticle

Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games

1
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan
2
Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan
3
Department of International Business, National Taichung University of Science and Technology, Taichung City 40401, Taiwan
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(10), 1128; https://doi.org/10.3390/electronics8101128
Received: 23 September 2019 / Accepted: 1 October 2019 / Published: 7 October 2019
(This article belongs to the Section Artificial Intelligence)
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.
Keywords: convolution neural network; deep principal component analysis network; image sensor; reinforcement learning; Q-learning; video game convolution neural network; deep principal component analysis network; image sensor; reinforcement learning; Q-learning; video game
MDPI and ACS Style

Lin, C.-J.; Jhang, J.-Y.; Lee, C.-L.; Lin, H.-Y.; Young, K.-Y. Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games. Electronics 2019, 8, 1128.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop