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 \textbf{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 $100$B transitions and $2.5$B 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
this https URL

通过构建 XLand-100B 数据集，该研究致力于解决在大规模环境中进行上下文强化学习时所面临的挑战，为进一步扩展和民主化该领域的研究提供坚实的基础。