  Cold atom traps are at the heart of many quantum applications in science and
technology. The preparation and control of atomic clouds involves complex
optimization processes, that could be supported and accelerated by machine
learning. In this work, we introduce reinforcement learning to cold atom
experiments and demonstrate a flexible and adaptive approach to control a
magneto-optical trap. Instead of following a set of predetermined rules to
accomplish a specific task, the objectives are defined by a reward function.
This approach not only optimizes the cooling of atoms just as an
experimentalist would do, but also enables new operational modes such as the
preparation of pre-defined numbers of atoms in a cloud. The machine control is
trained to be robust against external perturbations and able to react to
situations not seen during the training. Finally, we show that the time
consuming training can be performed in-silico using a generic simulation and
demonstrate successful transfer to the real world experiment.
