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arXiv · 2107.04457

Aligning an optical interferometer with beam divergence control and continuous action space

Stepan Makarenko·Dmitry Sorokin·Alexander Ulanov·A. I. Lvovsky
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Abstract

Reinforcement learning is finding its way to real-world problem application, transferring from simulated environments to physical setups. In this work, we implement vision-based alignment of an optical Mach-Zehnder interferometer with a confocal telescope in one arm, which controls the diameter and divergence of the corresponding beam. We use a continuous action space; exponential scaling enables us to handle actions within a range of over two orders of magnitude. Our agent trains only in a simulated environment with domain randomizations. In an experimental evaluation, the agent significantly outperforms an existing solution and a human expert.

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Stepan Makarenko, Dmitry Sorokin, Alexander Ulanov, A. I. Lvovsky. 2021-11-16. Aligning an optical interferometer with beam divergence control and continuous action space. https://arxiv.org/abs/2107.04457

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