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@INPROCEEDINGS{Hirlaender:607018,
      author       = {Hirlaender, Simon and Pochaba, Sabrina and Lamminger, Lukas
                      and Kaiser, Jan and Xu, Chenran and Santamaria Garcia,
                      Andrea and Scomparin, Luca},
      title        = {{T}owards few-shot reinforcement learning in particle
                      accelerator control},
      address      = {Geneva, Switzerland},
      publisher    = {JACoW Publishing},
      reportid     = {PUBDB-2024-01723},
      isbn         = {978-3-95450-247-9},
      pages        = {1804 - 1807},
      year         = {2024},
      note         = {Literaturangaben;},
      comment      = {[Ebook] 15th International Particle Accelerator Conference,
                      Nashville, Tennessee : May 19-24, 2024, Nashville,
                      Tennessee, USA : proceedings / Pilat, Fulvia ; Andrian, Ivan
                      , [Geneva] : JACoW Publishing, [2024],},
      booktitle     = {[Ebook] 15th International Particle
                       Accelerator Conference, Nashville,
                       Tennessee : May 19-24, 2024, Nashville,
                       Tennessee, USA : proceedings / Pilat,
                       Fulvia ; Andrian, Ivan , [Geneva] :
                       JACoW Publishing, [2024],},
      abstract     = {This paper addresses the automation of particle accelerator
                      control through reinforcement learning (RL). It highlights
                      the potential to increase reliable performance, especially
                      in light of new diagnostic tools and the increasingly
                      complex variable schedules of specific accelerators. We
                      focus on the physics simulation of the AWAKE electron line,
                      an ideal platform for performing in-depth studies that allow
                      a clear distinction between the problem and the performance
                      of different algorithmic approaches for accurate analysis.
                      The main challenges are the lack of realistic simulations
                      and partially observable environments. We show how effective
                      results can be achieved through meta-reinforcement learning,
                      where an agent is trained to quickly adapt to specific
                      real-world scenarios based on prior training in a simulated
                      environment with variable unknowns. When suitable
                      simulations are lacking or too costly, a model-based method
                      using Gaussian processes is used for direct training in a
                      few shots only. The work opens new avenues for implementing
                      control automation in particle accelerators, significantly
                      increasing their efficiency and adaptability.},
      month         = {May},
      date          = {2024-05-19},
      organization  = {15th International Particle
                       Accelerator Conference, Nashville
                       (USA), 19 May 2024 - 24 May 2024},
      keywords     = {Accelerator Physics (Other) /
                      mc6-beam-instrumentation-controls-feedback-and-operational-aspects
                      - MC6: Beam Instrumentation, Controls, Feedback, and
                      Operational Aspects (Other) / MC6.D13 - MC6.D13 Machine
                      Learning (Other)},
      cin          = {MSK / KIT / CERN},
      cid          = {I:(DE-H253)MSK-20120731 / I:(DE-H253)KIT-20130928 /
                      I:(DE-H253)CERN-20181204},
      pnm          = {621 - Accelerator Research and Development (POF4-621) /
                      InternLabs-0011 - HIR3X - Helmholtz International Laboratory
                      on Reliability, Repetition, Results at the most advanced
                      X-ray Sources $(2020_InternLabs-0011)$},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2020_InternLabs-0011$},
      experiment   = {EXP:(DE-H253)ARES-20200101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.18429/JACOW-IPAC2024-TUPS60},
      url          = {https://bib-pubdb1.desy.de/record/607018},
}