@article{RLMOT,
  title = {Reinforcement learning in cold atom experiments},
  journal = {nature communications},
  abstract = {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 experi-
  mentalist 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 con-
  suming training can be performed in-silico using a generic simulation and
  demonstrate successful transfer to the real world experiment.},
  volume = {15:8532},
  month = jan,
  year = {2024},
  slug = {rlmot},
  author = {Reinschmidt, Malte and Fortágh, József and G{\"u}nther, Andreas and Volchkov, Valentin},
  url = {https://doi.org/10.1038/s41467-024-52775-8},
  month_numeric = {1}
}