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

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