Chinese Journal of Lasers, Volume. 52, Issue 3, 0301001(2025)
Stabilization of Intelligent Laser Cavity Based on Deep Reinforcement Learning Algorithm
Fig. 3. Impact of different parameters on resonator. (a) Radius of fundamental mode versus thermal focal length; (b) mirror misalignment versus power detuning
Fig. 6. Comparison among intelligent agent, SPGD, and GA under different detuning conditions, with illustration showing two-dimensional distributions of corresponding far-field beam patterns
Fig. 7. Statistical distribution of recovery time in experimental and simulation tests
Fig. 8. Evolution of output power over time in intelligent agent control process, with illustration showing spatial distributions of 3D beam
Fig. 10. Typical M2 measurement result after optimization, with illustration of corresponding far-field two-dimensional beam spatial distribution
|
Get Citation
Copy Citation Text
Jingyu Li, Zongzhe Zhang, Jing Yang, Lin Han, Hao Wang, Yunping Wang, Hongwei Gao, Xiaojun Wang, Zuyan Xu. Stabilization of Intelligent Laser Cavity Based on Deep Reinforcement Learning Algorithm[J]. Chinese Journal of Lasers, 2025, 52(3): 0301001
Category: laser devices and laser physics
Received: Apr. 9, 2024
Accepted: Jun. 3, 2024
Published Online: Jan. 14, 2025
The Author Email: Yang Jing (yangjing@mail.ipc.ac.cn)
CSTR:32183.14.CJL240755