The sample inefficiency of standard deep reinforcement learning methods
precludes their application to many real-world problems. Methods which leverage
human demonstrations require fewer samples but have been researched less. As
demonstrated in the computer vision and natural language processing
communities, large-scale datasets have the capacity to facilitate research by
serving as an experimental and benchmarking platform for new methods. However,
existing datasets compatible with reinforcement learning simulators do not have
sufficient scale, structure, and quality to enable the further development and
evaluation of methods focused on using human examples. Therefore, we introduce
a comprehensive, large-scale, simulator-paired dataset of human demonstrations:
MineRL. The dataset consists of over 60 million automatically annotated
state-action pairs across a variety of related tasks in Minecraft, a dynamic,
3D, open-world environment. We present a novel data collection scheme which
allows for the ongoing introduction of new tasks and the gathering of complete
state information suitable for a variety of methods. We demonstrate the
hierarchality, diversity, and scale of the MineRL dataset. Further, we show the
difficulty of the Minecraft domain along with the potential of MineRL in
developing techniques to solve key research challenges within it.

本研究介绍了一个全面、大规模、与模拟器配对并包含人类示范的数据集 MineRL，该数据集涵盖了 Minecraft 中多个相关任务，其中有超过 6000 万个由自动注释的状态动作对，证明了该数据集的层次性、多样性和规模。该数据集有助于开展解决 Minecraft 研究挑战的技术研究。