Coherent beam combination based on Q-learning algorithm

被引：17

作者：

Zhang, Xi ^{[1
]}

Li, Pingxue ^{[1
]}

Zhu, Yunchen ^{[1
]}

Li, Chunyong ^{[2
]}

Yao, Chuanfei ^{[1
]}

Wang, Luo ^{[1
]}

Dong, Xueyan ^{[1
]}

Li, Shun ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Mat & Mfg, Inst Ultrashort Pulsed Laser & Applicat, Beijing 10024, Peoples R China

[2] Univ Durham, Dept Phys, South Rd, Durham DH1 3LE, England

来源：

OPTICS COMMUNICATIONS | 2021年 / 490卷

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

Coherent beam combination; Q-learning algorithm; Stochastic parallel gradient descent; optimization algorithm; FIBER LASERS;

D O I：

10.1016/j.optcom.2021.126930

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Coherent beam combination (CBC) is an effective method to break the limiting power of a single fiber laser. The Q-learning algorithm is one of the reinforcement learning algorithms. We use the Q-learning algorithm to do phase compensation in the field of CBC. The performance difference between the Q-learning algorithm and the stochastic parallel gradient descent optimization algorithm (SPGD) is analyzed by simulating time-domain coherent synthesis. The results show that the Q-learning algorithm is easier to debug and has better stability.

引用

页数：7

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