Deep reinforcement with spectrum series learning control for a mode-locked fiber laser

Abstract

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 reinforcement 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 algorithm 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°C–30°C 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.

Artificial Intelligence-Enabled Mode-Locked Fiber Laser: A Review

“…The algorithm learns the hysteresis phenomena (represented by different optical pump adjustment trajectories) of different pump power thresholds under a mode-locked state. Li et al [94] presented a spectrum series learning-based model combining deep reinforcement learning and LSTM networks for the state searching and switching of mode-locked fiber lasers. The switch of the mode-locked state is realized by a predictive neural network that controls the pump power.…”

Machine Learning Based Automatic Mode-Locking of a Dual-Wavelength Soliton Fiber Laser

“…Machine learning techniques have also been applied to the control and optimization of mode-locked fiber lasers [19,22,28]. Automatic mode-locking has been achieved with various algorithms such as genetic algorithms [29][30][31], human-like algorithms [32], and reinforcement learning [33,34]. Machine learning techniques have also been used for the on-demand generation of soliton molecules [35], generation of breathing solitons [36], and bandwidth optimization of a broadband noise-like pulse laser [37].…”

Enhanced Mode-Locked Laser Control with Deep Reinforcement Learning: A Comparative Study with DDQN, TD3 and SAC Algorithms

“…Evgeny Kuprikov used the DDQN algorithm, but reduced the oscillation by changing the reward function to the ratio of pulse energy to pulse noise in the oscillation trajectory (Kuprikov et al 2022). Zhan Li proposes a mode-locked operating mode search (MDRL) and switching algorithm (MSP) based on Actor-Critic (AC) and spectrum series learning (Li et al 2022). It can obtain the mode-locked state of fiber laser, but quickly adapt to different environments and switch between different working states.…”