  Experimental control involves a lot of manual effort with non-trivial
decisions for precise adjustments. Here, we study the automatic experimental
alignment for coupling laser light into an optical fiber using reinforcement
learning (RL). We face several real-world challenges, such as time-consuming
training, partial observability, and noisy actions due to imprecision in the
mirror steering motors. We show that we can overcome these challenges: To save
time, we use a virtual testbed to tune our environment for dealing with partial
observability and use relatively sample-efficient model-free RL algorithms like
Soft Actor-Critic (SAC) or Truncated Quantile Critics (TQC). Furthermore, by
fully training on the experiment, the agent learns directly to handle the noise
present. In our extensive experimentation, we show that we are able to achieve
90% coupling, showcasing the effectiveness of our proposed approaches. We reach
this efficiency, which is comparable to that of a human expert, without
additional feedback loops despite the motors' inaccuracies. Our result is an
example of the readiness of RL for real-world tasks. We consider RL a promising
tool for reducing the workload in labs.
