Linearization of nonlinear frequency modulated continuous wave generation using model-based reinforcement learning

The prevalence of machine learning (ML) opens up new directions for plenty of scientific fields. The development of optics technologies also benefits from it. However, due to the complex properties of nonlinear and dynamic optical systems, optical system control with ML is still in its infancy. In this manuscript, to demonstrate the feasibility of optical system control using reinforcement learning (RL), i.e., a branch of ML, we solve the linearization problem in the frequency modulated continuous wave (FMCW) generation with the model-based RL method. The experiment results indicate an excellent improvement in the linearity of the generated FMCW, showing a sharp peak in the frequency spectrum. We confirm that the RL method learns the implicit physical characteristics very well and accomplishes the goal of the linear FMCW generation effectively, indicating that the marriage of ML and optics systems could have the potential to open a new era for the development of optical system control.