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@INPROCEEDINGS{Kaiser:478845,
      author       = {Kaiser, Jan and Stein, Oliver and Eichler, Annika},
      title        = {{L}earning-based {O}ptimisation of {P}article
                      {A}ccelerators {U}nder {P}artial {O}bservability {W}ithout
                      {R}eal-{W}orld {T}raining},
      publisher    = {PMLR},
      reportid     = {PUBDB-2022-02795},
      pages        = {10575-10585},
      year         = {2022},
      comment      = {Proceedings of the 39th International Conference on Machine
                      Learning},
      booktitle     = {Proceedings of the 39th International
                       Conference on Machine Learning},
      abstract     = {In recent work, it has been shown that reinforcement
                      learning (RL) is capable of solving a variety of problems at
                      sometimes super-human performance levels. But despite
                      continued advances in the field, applying RL to complex
                      real-world control and optimisation problems has proven
                      difficult. In this contribution, we demonstrate how to
                      successfully apply RL to the optimisation of a highly
                      complex real-world machine – specifically a linear
                      particle accelerator – in an only partially observable
                      setting and without requiring training on the real machine.
                      Our method outperforms conventional optimisation algorithms
                      in both the achieved result and time taken as well as
                      already achieving close to human-level performance. We
                      expect that such automation of machine optimisation will
                      push the limits of operability, increase machine
                      availability and lead to a paradigm shift in how such
                      machines are operated, ultimately facilitating advances in a
                      variety of fields, such as science and medicine among many
                      others.},
      month         = {Jul},
      date          = {2022-07-17},
      organization  = {39th International Conference on
                       Machine Learning, Baltimore (USA), 17
                       Jul 2022 - 23 Jul 2022},
      cin          = {MSK / D3},
      cid          = {I:(DE-H253)MSK-20120731 / I:(DE-H253)D3-20120731},
      pnm          = {621 - Accelerator Research and Development (POF4-621)},
      pid          = {G:(DE-HGF)POF4-621},
      experiment   = {EXP:(DE-H253)ARES-20200101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://bib-pubdb1.desy.de/record/478845},
}