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@INPROCEEDINGS{SantamariaGarcia:607016,
      author       = {Santamaria Garcia, Andrea and Scomparin, Luca and Xu,
                      Chenran and Hirlaender, Simon and Pochaba, Sabrina and
                      Eichler, Annika and Kaiser, Jan and Schenk, Michael},
      title        = {{T}he {R}einforcement {L}earning for {A}utonomous
                      {A}ccelerators {C}ollaboration},
      address      = {Geneva, Switzerland},
      publisher    = {JACoW Publishing},
      reportid     = {PUBDB-2024-01721},
      isbn         = {978-3-95450-247-9},
      pages        = {1812 - 1815},
      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     = {Reinforcement Learning (RL) is a unique learning paradigm
                      that is particularly well-suited to tackle complex control
                      tasks, can deal with delayed consequences, and can learn
                      from experience without an explicit model of the dynamics of
                      the problem. These properties make RL methods extremely
                      promising for applications in particle accelerators, where
                      the dynamically evolving conditions of both the particle
                      beam and the accelerator systems must be constantly
                      considered. While the time to work on RL is now particularly
                      favorable thanks to the availability of high-level
                      programming libraries and resources, its implementation in
                      particle accelerators is not trivial and requires further
                      consideration. In this context, the Reinforcement Learning
                      for Autonomous Accelerators (RL4AA) international
                      collaboration was established to consolidate existing
                      knowledge, share experiences and ideas, and collaborate on
                      accelerator-specific solutions that leverage recent advances
                      in RL. Here we report on two collaboration workshops,
                      RL4AA'23 and RL4AA'24, which took place in February 2023 at
                      the Karlsruhe Institute of Technology and in February 2024
                      at the Paris-Lodron Universität Salzburg.},
      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},
      cid          = {I:(DE-H253)MSK-20120731 / I:(DE-H253)KIT-20130928},
      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},
      doi          = {10.18429/JACOW-IPAC2024-TUPS62},
      url          = {https://bib-pubdb1.desy.de/record/607016},
}