XLand-100B: A Large-Scale Multi-Task Dataset for In-Context
  Reinforcement Learning
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.
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Alexander Nikulin
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2024-06-17T14:12:59.522Z
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Alexander Nikulin
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Ilya Zisman
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2024-06-17T16:15:22.758Z
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Ilya
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suessmann
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Alexey Zemtsov
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2024-06-17T13:43:22.212Z
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Alex
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Viacheslav Sinii
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2025-02-04T09:40:13.744Z
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Viacheslav
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Vladislav Kurenkov
claimed_verified
2024-06-17T13:43:11.259Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/65d3711b37e2a376885b2ca7/IPL35lsDjTtcmB8uVDpiY.jpeg
Vladislav Kurenkov
user
vkurenkov
666f1c8560142338147bfff8
Sergey Kolesnikov
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context
  Reinforcement Learning
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.
XLand-100B is a large-scale dataset for in-context reinforcement learning, containing extensive learning histories in the XLand-MiniGrid environment.
in-context learning reinforcement learning XLand-100B XLand-MiniGrid transitions episodes GPU hours