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 | |
刊名 | IEEE PHOTONICS TECHNOLOGY LETTERS |
2018-12-01 | |
卷号 | 30期号:23页码:2033-2036 |
关键词 | WFSless AO deep reinforcement learning DDPG CNN SPGD AOG |
ISSN号 | 1041-1135 |
DOI | 10.1109/LPT.2018.2874998 |
文献子类 | J |
英文摘要 | We 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. |
WOS关键词 | ABERRATION CORRECTION ; ALGORITHM |
语种 | 英语 |
WOS记录号 | WOS:000451233300010 |
内容类型 | 期刊论文 |
源URL | [http://ir.ioe.ac.cn/handle/181551/9286] |
专题 | 光电技术研究所_自适应光学技术研究室(八室) |
作者单位 | 1.Key Laboratory of Adaptive Optics, Chinese Academy of Sciences, Chengdu; 610209, China; 2.University of Chinese Academy of Sciences, Beijing; 100049, China |
推荐引用方式 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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