Reinforcement Learning for guiding optimization processes in optical design

Nowadays, sophisticated ray-tracing software packages are used for the design of optical systems, including local and global optimization algorithms. Nevertheless, the design process is still time-consuming with many manual steps, and it can take days or even weeks until an optical design is finished. To address this shortcoming, artificial intelligence, especially reinforcement learning, is employed to support the optical designer. In this work, different use cases are presented, in which reinforcement learning agents are trained to optimize a lens system. Besides the possibility of bending lenses to reduce spherical aberration, the movement of lenses to optimize the lens positions for a varifocal lens system is shown. Finally, the optimization of lens surface curvatures and distances between lenses are analyzed. For a predefined Cooke Triplet, an agent can choose the curvature of the different surfaces as optimization parameters. The chosen surfaces and the distances between the lenses will then be optimized with a least-squares optimizer.(1). It is shown, that for a Cooke Triplet, setting all surfaces as variables is a good suggestion for most systems if the runtime is not an issue. Taking the runtime into account, the selected number of variable surfaces decreases. For optical systems with a large number of degrees of freedom an intelligent selection of optimization variables can probably be a powerful tool for an efficient and time-saving optimization.