  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.
