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

Area of Science:

• Optics and Photonics
• Artificial Intelligence
• Control Systems

Background:

• Machine learning (ML) offers new possibilities for scientific advancements, including optics.
• Controlling complex nonlinear and dynamic optical systems with ML remains challenging.
• Reinforcement learning (RL) is a branch of ML with potential for optical system control.

Purpose of the Study:

• To demonstrate the feasibility of optical system control using reinforcement learning (RL).
• To solve the linearization problem in frequency modulated continuous wave (FMCW) generation using a model-based RL method.

Main Methods:

• Employed a model-based reinforcement learning (RL) approach.
• Applied RL to address linearization challenges in FMCW generation.
• Utilized experimental validation to confirm the method's effectiveness.

Main Results:

• Achieved excellent improvement in the linearity of generated FMCW signals.
• Observed a sharp peak in the frequency spectrum, indicating enhanced linearity.
• Confirmed that RL effectively learns implicit physical characteristics of the optical system.

Conclusions:

• Reinforcement learning (RL) is a viable method for controlling optical systems.
• The developed RL approach successfully achieves linear FMCW generation.
• The integration of ML, particularly RL, with optics systems heralds a new era in optical control.