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@INPROCEEDINGS{Hirlaender:607325,
      author       = {Hirlaender, Simon and Pochaba, Sabrina and Lamminger, Lukas
                      and Santamaria Garcia, Andrea and Xu, Chenran and Kaiser,
                      Jan and Eichler, Annika and Kain, Verena},
      title        = {{D}eep {M}eta {R}einforcement {L}earning for {R}apid
                      {A}daptation {I}n {L}inear {M}arkov {D}ecision {P}rocesses:
                      {A}pplications to {CERN}’s {AWAKE} {P}roject; 1st ed.
                      2024},
      volume       = {1458},
      address      = {Cham},
      publisher    = {Springer Nature Switzerland},
      reportid     = {PUBDB-2024-01836},
      isbn         = {978-3-031-65992-8},
      series       = {Advances in Intelligent Systems and Computing},
      pages        = {175 - 183},
      year         = {2024},
      note         = {Missing Journal: = 2194-5365 (import from CrossRef Book
                      Series, Journals: bib-pubdb1.desy.de)},
      comment      = {[Ebook] Combining, Modelling and Analyzing Imprecision,
                      Randomness and Dependence / Ansari, Jonathan ; Fuchs,
                      Sebastian ; Trutschnig, Wolfgang ; Lubiano, María Asunción
                      ; Gil, María Ángeles ; Grzegorzewski, Przemyslaw ;
                      Hryniewicz, Olgierd 1st ed. 2024, Cham : Springer Nature
                      Switzerland, 2024,},
      booktitle     = {[Ebook] Combining, Modelling and
                       Analyzing Imprecision, Randomness and
                       Dependence / Ansari, Jonathan ; Fuchs,
                       Sebastian ; Trutschnig, Wolfgang ;
                       Lubiano, María Asunción ; Gil, María
                       Ángeles ; Grzegorzewski, Przemyslaw ;
                       Hryniewicz, Olgierd 1st ed. 2024, Cham
                       : Springer Nature Switzerland, 2024,},
      abstract     = {Real-world applications of reinforcement learning (RL) face
                      challenges such as the need for numerous interactions and
                      achieving stable training under dynamic conditions. Meta-RL
                      emerges as a solution, particularly in environments where
                      simulations cannot perfectly mimic real-world conditions.
                      This study demonstrates Meta-RL’s potential in the
                      CERN’s AWAKE project, focusing on the electron line’s
                      control. By incorporating Model-Agnostic Meta-Learning
                      (MAML), we showcase how Meta-RL facilitates rapid adaptation
                      to environmental changes with minimal interaction steps. Our
                      findings indicate Meta-RL’s efficacy in managing Partially
                      Observable Markov Decision Processes (POMDPs) with evolving
                      hidden parameters, underlining its significance in
                      high-dimensional control challenges prevalent in particle
                      physics experiments and beyond.},
      month         = {Sep},
      date          = {2024-09-03},
      organization  = {11th International Conference on Soft
                       Methods in Probability and Statistics,
                       Salzburg (Austria), 3 Sep 2024 - 6 Sep
                       2024},
      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          = {10.1007/978-3-031-65993-5_21},
      url          = {https://bib-pubdb1.desy.de/record/607325},
}