標題: Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games
作者: Lin, Cheng-Jian
Jhang, Jyun-Yu
Lin, Hsueh-Yi
Lee, Chin-Ling
Young, Kuu-Young
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: convolution neural network;deep principal component analysis network;image sensor;reinforcement learning;Q-learning;video game
公開日期: 1-Oct-2019
摘要: 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.
URI: http://dx.doi.org/10.3390/electronics8101128
http://hdl.handle.net/11536/153401
DOI: 10.3390/electronics8101128
期刊: ELECTRONICS
Volume: 8
Issue: 10
起始頁: 0
結束頁: 0
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