Reinforcement learning competitions advance the field by providing
appropriate scope and support to develop solutions toward a specific problem.
To promote the development of more broadly applicable methods, organizers need
to enforce the use of general techniques, the use of sample-efficient methods,
and the reproducibility of the results. While beneficial for the research
community, these restrictions come at a cost -- increased difficulty. If the
barrier for entry is too high, many potential participants are demoralized.
With this in mind, we hosted the third edition of the MineRL ObtainDiamond
competition, MineRL Diamond 2021, with a separate track in which we permitted
any solution to promote the participation of newcomers. With this track and
more extensive tutorials and support, we saw an increased number of
submissions. The participants of this easier track were able to obtain a
diamond, and the participants of the harder track progressed the generalizable
solutions in the same task.

通过主办钻石获取挑战赛，我们鼓励参与者使用常规技术和高效示例方法开发更广泛适用的解决方案，促进参与者和新手参与。这项研究强调了使用这些技术的重要性并提供了支持。

MineRL Diamond 2021 竞赛：概述、结果和经验教训

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

Though deep reinforcement learning has led to breakthroughs in many difficult
domains, these successes have required an ever-increasing number of samples. As
state-of-the-art reinforcement learning (RL) systems require an exponentially
increasing number of samples, their development is restricted to a continually
shrinking segment of the AI community. Likewise, many of these systems cannot
be applied to real-world problems, where environment samples are expensive.
Resolution of these limitations requires new, sample-efficient methods. To
facilitate research in this direction, we introduce the MineRL Competition on
Sample Efficient Reinforcement Learning using Human Priors.
The primary goal of the competition is to foster the development of
algorithms which can efficiently leverage human demonstrations to drastically
reduce the number of samples needed to solve complex, hierarchical, and sparse
environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task,
a sequential decision making environment requiring long-term planning,
hierarchical control, and efficient exploration methods; and (2) the MineRL-v0
dataset, a large-scale collection of over 60 million state-action pairs of
human demonstrations that can be resimulated into embodied trajectories with
arbitrary modifications to game state and visuals.
Participants will compete to develop systems which solve the ObtainDiamond
task with a limited number of samples from the environment simulator, Malmo.
The competition is structured into two rounds in which competitors are provided
several paired versions of the dataset and environment with different game
textures. At the end of each round, competitors will submit containerized
versions of their learning algorithms and they will then be trained/evaluated
from scratch on a hold-out dataset-environment pair for a total of 4-days on a
prespecified hardware platform.

介绍了 MineRL 竞赛，该竞赛旨在利用人类先验知识实现样本高效的强化学习，提供了 Minecraft ObtrainDiamond 任务和 MineRL-v0 数据集，并要求参赛者使用 Malmo 环境中的有限样本来解决 ObtainDiamond 任务。