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Convolutional fitted Q iteration for vision-based control problems
Zhao Dongbin; Zhu Yuanheng; Lv Le; Chen Yaran; Zhang Qichao
Conference NameThe 2016 International Joint Conference on Neural Networks
Conference Date24-29 July 2016
Conference PlaceVancouver, BC, Canada
AbstractIn this paper a deep reinforcement learning (DRL) method is proposed to solve the control problem which takes raw image pixels as input states. A convolutional neural network (CNN) is used to approximate Q functions, termed as Q-CNN. A pretrained network, which is the result of a classification challenge on a vast set of natural images, initializes the parameters of Q-CNN. Such initialization assigns Q-CNN with the features of image representation, so it is more concentrated on the control tasks. The weights are tuned under the scheme of fitted Q iteration (FQI), which is an offline reinforcement learning method with the stable convergence property. To demonstrate the performance, a modified Food-Poison problem is simulated. The agent determines its movements based on its forward view. In the end the algorithm successfully learns a satisfied policy which has better performance than the results of previous researches.
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Document Type会议论文
Affiliationhe State Key Laboratory of Management and Control for Complex Systems, In- stitution of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Recommended Citation
GB/T 7714
Zhao Dongbin,Zhu Yuanheng,Lv Le,et al. Convolutional fitted Q iteration for vision-based control problems[C],2016.
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