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《Electronics World》 2017-16
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The Application of Depth of reinforcement Learning in the Vedio Game

Shi Zhengjin;Wang Kang;School Of Automation And Electrical Engineering,Shenyang Ligong University;  
Considering the advantage of depth learning in image feature extraction,In order to improve the depth study on the Atari game performance this paper proposes a depth neural network structure based on model fusion,convolution neural network and modified Q-learning algorithm.Experiments show that the new model can fully study the control strategy,and it achieve or exceed the scores of the general learning model in the Atari game.Proving the deep reinforcement learning based on model fusion have the stability and superiority in the video game.
【CateGory Index】: TP18;TP391.41
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