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@INPROCEEDINGS{Ebert:1020524,
      author       = {Ebert, Jan and Doncevic, Danimir T. and Kloß, Ramona and
                      Kesselheim, Stefan},
      title        = {{H}earts {G}ym: {L}earning {R}einforcement {L}earning as a
                      {T}eam {E}vent; 3rd ed.},
      volume       = {207},
      publisher    = {PMLR},
      reportid     = {FZJ-2024-00243},
      pages        = {16-21},
      year         = {2023},
      abstract     = {Amidst the COVID-19 pandemic, the authors of this paper
                      organized a Reinforcement Learning (RL) course for a
                      graduate school in the field of data science. We describe
                      the strategy and materials for creating an exciting learning
                      experience despite the ubiquitous Zoom fatigue and evaluate
                      the course qualitatively. The key organizational features
                      are a focus on a competitive hands-on setting in teams,
                      supported by a minimum of lectures providing the essential
                      background on RL. The practical part of the course revolved
                      around Hearts Gym, an RL environment for the card game
                      Hearts that we developed as an entry-level tutorial to RL.
                      Participants were tasked with training agents to explore
                      reward shaping and other RL hyperparameters. For a final
                      evaluation, the agents of the participants competed against
                      each other.},
      month         = {Sep},
      date          = {2022-09-19},
      organization  = {Third Teaching Machine Learning and
                       Artificial Intelligence Workshop at
                       ECML, Grenoble (France), 19 Sep 2022 -
                       23 Sep 2022},
      cin          = {JSC / IEK-10 / IBG-1},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IEK-10-20170217 /
                      I:(DE-Juel1)IBG-1-20101118},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / HDS LEE - Helmholtz School
                      for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.34734/FZJ-2024-00243},
      url          = {https://juser.fz-juelich.de/record/1020524},
}