A spectrum series learning-based model is presented for mode-locked fiber laser state searching and switching.The mode-locked operation search policy is obtained by our proposed algorithm that combines deep reinforce-ment learning and long short-term memory networks.Numerical simulations show that the dynamic features of the laser cavity can be obtained from spectrum series.Compared with the traditional evolutionary search algo-rithm that only uses the current state,this model greatly improves the efficiency of the mode-locked search.The switch of the mode-locked state is realized by a predictive neural network that controls the pump power.In the experiments,the proposed algorithm uses an average of only 690 ms to obtain a stable mode-locked state,which is one order of magnitude less than that of the traditional method.The maximum number of search steps in the algorithm is 47 in the 16℃-30℃temperature environment.The pump power prediction error is less than 2 mW,which ensures precise laser locking on multiple operating states.This proposed technique paves the way for a variety of optical systems that require fast and robust control.

Key Laboratory of High Power Laser and Physics,Shanghai Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Shanghai 201800,China;Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China

[1]ZHAN LI;SHUAISHUAI YANG;QI XIAO;TIANYU ZHANG;YONG LI;LU HAN;DEAN LIU;XIAOPING OUYANG;JIANQIANG ZHU-.Deep reinforcement with spectrum series learning control for a mode-locked fiber laser)[J].光子学研究（英文）,2022(06):1491-1500

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