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Build the Structure of WFSless AO System Through Deep Reinforcement Learning
Hu, K.1,2; Xu, Z.X.1; Yang, W.2; Xu, B.1
Indexed BySCI ; Ei
WOS IDWOS:000451233300010
EI Accession Number20184205949711
AbstractWe report on an aberration correction algorithm for a wavefront sensorless adaptive optics (WFSless AO) system based on deep reinforcement learning. First, it is verified that the reinforcement learning theory can be applied in our system. In addition, the deep deterministic policy gradient algorithm is introduced to build the control structure. After that, deep learning is used to deal with the messy raw images of far-field intensity distribution. We emphatically present how to design a feature extraction with the convolutional neural network in the control structure. To demonstrate the performance of this structure, some comparisons are made with the stochastic parallel gradient descent algorithm and the WFSless AO based on general modes algorithm. The results indicate that the correction speed of our method improves about 9 times and 2.5 times, respectively, for the similar correction effect.
KeywordWFSless AO deep reinforcement learning DDPG CNN SPGD AOG
EI KeywordsAdaptive optics ; Gradient methods ; Neural networks ; Reinforcement learning ; Stochastic systems
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Document Type期刊论文
Affiliation1.Key Laboratory of Adaptive Optics, Chinese Academy of Sciences, Chengdu; 610209, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China
Recommended Citation
GB/T 7714
Hu, K.,Xu, Z.X.,Yang, W.,et al. Build the Structure of WFSless AO System Through Deep Reinforcement Learning[J]. IEEE PHOTONICS TECHNOLOGY LETTERS,2018,30(23):2033-2036.
APA Hu, K.,Xu, Z.X.,Yang, W.,&Xu, B..(2018).Build the Structure of WFSless AO System Through Deep Reinforcement Learning.IEEE PHOTONICS TECHNOLOGY LETTERS,30(23),2033-2036.
MLA Hu, K.,et al."Build the Structure of WFSless AO System Through Deep Reinforcement Learning".IEEE PHOTONICS TECHNOLOGY LETTERS 30.23(2018):2033-2036.
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