Reinforcement learning for adaptive optical coating design

Building on advancements in experimental optics, this work presents a friendlier and more accessible approach to applying reinforcement learning (RL) in designing adaptive optical coatings. Adaptive optics, which help reduce wave-front imperfections to improve image clarity, can now be integrated into complex optical systems with computer-controlled liquid crystal optical elements. However, the relationship between the properties and performance of adaptive coatings is quite intricate and nonlinear, making manual adjustments challenging and traditional finite-difference time-domain methods often too slow for practical optimization. RL, which involves training agents through trial and error to reach specific goals, has shown great promise in controlling nonlinear systems. That’s why, in this study, we use an RL approach to optimize the parameters of multilayer electro-optical coatings. Compared to conventional algorithms, our RL method offers a more flexible and efficient way to find solutions for multi-layer coatings, even with many layers, and can be further improved by refining the current framework. Our results demonstrate that RL is a feasible and effective tool to address this challenging problem.

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