Laser simulation and reinforcement learning mode locking

In this paper, the generation of ultrafast pulsed lasers in the mid-infrared (mid-IR) region around 3μm is simulated by MATLAB and combined with Reinforcement Learning (RL) to train the mode-locking mechanism. Firstly, the mid-IR laser design and its generated ultrafast pulse characteristics mentioned in a paper are reproduced by numerical simulation. The simulation results verify the performance of the laser under specific parameter settings. Subsequently, we introduced a reinforcement learning algorithm to achieve an efficient mode-locked training process by continuously adjusting the laser parameters through an intelligent agent (agent). During the training process, the intelligent agent dynamically optimizes the laser parameters according to the feedback signals to achieve the optimal mode-locking state. The final experimental results show that the reinforcement learning algorithm has significant advantages in the laser mode-locking process, which not only improves the mode-locking efficiency, but also enhances the stability of the system. This study demonstrates that the combination of numerical simulation and reinforcement learning can effectively realize ultrafast pulsed laser mode-locking in the mid-infrared region, thus reflecting the richer application value of artificial intelligence in laser mode-locking and the potential for further research.