  Machine-learning techniques are emerging as a valuable tool in experimental
physics, and among them, reinforcement learning offers the potential to control
high-dimensional, multistage processes in the presence of fluctuating
environments. In this experimental work, we apply reinforcement learning to the
preparation of an ultracold quantum gas to realize a consistent and large
number of atoms at microkelvin temperatures. This reinforcement learning agent
determines an optimal set of thirty control parameters in a dynamically
changing environment that is characterized by thirty sensed parameters. By
comparing this method to that of training supervised-learning regression
models, as well as to human-driven control schemes, we find that both machine
learning approaches accurately predict the number of cooled atoms and both
result in occasional superhuman control schemes. However, only the
reinforcement learning method achieves consistent outcomes, even in the
presence of a dynamic environment.
