title: XLand-100B: A Large-Scale Multi-Task Dataset for In-Context
    Reinforcement Learning
summary: Following the success of the in-context learning paradigm in large-scale
  language and computer vision models, the recently emerging field of in-context
  reinforcement learning is experiencing a rapid growth. However, its development
  has been held back by the lack of challenging benchmarks, as all the
  experiments have been carried out in simple environments and on small-scale
  datasets. We present XLand-100B, a large-scale dataset for in-context
  reinforcement learning based on the XLand-MiniGrid environment, as a first step
  to alleviate this problem. It contains complete learning histories for nearly
  30,000 different tasks, covering 100B transitions and 2.5B episodes. It
  took 50,000 GPU hours to collect the dataset, which is beyond the reach of
  most academic labs. Along with the dataset, we provide the utilities to
  reproduce or expand it even further. With this substantial effort, we aim to
  democratize research in the rapidly growing field of in-context reinforcement
  learning and provide a solid foundation for further scaling. The code is
  open-source and available under Apache 2.0 licence at
  https://github.com/dunno-lab/xland-minigrid-datasets.
paper_authors._id: 666f1c8560142338147bfff3
paper_authors.name: Alexander Nikulin
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paper_authors.fullname: Alexander Nikulin
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paper_authors.user: Howuhh
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paper_authors.name: Ilya Zisman
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paper_authors.fullname: Ilya
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paper_authors.user: suessmann
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paper_authors.name: Alexey Zemtsov
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paper_authors.fullname: Alex
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paper_authors.user: zzmtsvv
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paper_authors.name: Viacheslav Sinii
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paper_authors.fullname: Viacheslav
paper_authors.type: user
paper_authors.user: ummagumm-a
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paper_authors.name: Vladislav Kurenkov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-17T13:43:11.259Z
paper_authors._id: 65d3711b37e2a376885b2ca7
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paper_authors.fullname: Vladislav Kurenkov
paper_authors.type: user
paper_authors.user: vkurenkov
paper_authors._id: 666f1c8560142338147bfff8
paper_authors.name: Sergey Kolesnikov
paper_title: XLand-100B: A Large-Scale Multi-Task Dataset for In-Context
    Reinforcement Learning
paper_summary: Following the success of the in-context learning paradigm in large-scale
  language and computer vision models, the recently emerging field of in-context
  reinforcement learning is experiencing a rapid growth. However, its development
  has been held back by the lack of challenging benchmarks, as all the
  experiments have been carried out in simple environments and on small-scale
  datasets. We present XLand-100B, a large-scale dataset for in-context
  reinforcement learning based on the XLand-MiniGrid environment, as a first step
  to alleviate this problem. It contains complete learning histories for nearly
  30,000 different tasks, covering 100B transitions and 2.5B episodes. It
  took 50,000 GPU hours to collect the dataset, which is beyond the reach of
  most academic labs. Along with the dataset, we provide the utilities to
  reproduce or expand it even further. With this substantial effort, we aim to
  democratize research in the rapidly growing field of in-context reinforcement
  learning and provide a solid foundation for further scaling. The code is
  open-source and available under Apache 2.0 licence at
  https://github.com/dunno-lab/xland-minigrid-datasets.
paper_ai_keywords: in-context learning in-context reinforcement learning XLand-100B XLand-MiniGrid learning histories transitions episodes GPU hours utilities open-source