Self-Adjusting Optical Systems Based on Reinforcement Learning

Abstract

Progress in the field of machine learning has enhanced the development of self-adjusting optical systems capable of autonomously adapting to changing environmental conditions. This study demonstrates the concept of self-adjusting optical systems and presents a new approach based on reinforcement learning methods. We integrated reinforcement learning algorithms into the setup for tuning the laser radiation into the fiber, as well as into the complex for controlling the laser-plasma source. That reduced the dispersion of the generated X-ray signal by 2–3 times through automatic adjustment of the position of the rotating copper target and completely eliminated the linear trend arising from the ablation of the target surface. The adjustment of the system was performed based on feedback signals obtained from the spectrometer, and the movement of the target was achieved using a neural network-controlled stepper motor. As feedback, the second harmonic of femtosecond laser radiation was used, the intensity of which has a square root dependence on the X-ray yield. The developed machine learning methodology allows the considered systems to optimize their performance and adapt in real time, leading to increased efficiency, accuracy, and reliability.