MineRL 2019 competition challenged participants to train sample-efficient
agents to play Minecraft, by using a dataset of human gameplay and a limit
number of steps the environment. We approached this task with behavioural
cloning by predicting what actions human players would take, and reached fifth
place in the final ranking. Despite being a simple algorithm, we observed the
performance of such an approach can vary significantly, based on when the
training is stopped. In this paper, we detail our submission to the
competition, run further experiments to study how performance varied over
training and study how different engineering decisions affected these results.

本文讲述了我们参加 MineRL 2019 比赛的经历，我们使用行为克隆的算法来预测人类玩家的行动并在最终排名中获得了第五名，同时我们也发现了这种方法性能会根据训练停止的时间而有明显变化，我们进行了更多的实验来研究不同的工程决策对性能的影响。

使用行为克隆玩 Minecraft

Playing Minecraft with Behavioural Cloning

To facilitate research in the direction of sample efficient reinforcement
learning, we held the MineRL Competition on Sample Efficient Reinforcement
Learning Using Human Priors at the Thirty-third Conference on Neural
Information Processing Systems (NeurIPS 2019). The primary goal of this
competition was to promote the development of algorithms that use human
demonstrations alongside reinforcement learning to reduce the number of samples
needed to solve complex, hierarchical, and sparse environments. We describe the
competition, outlining the primary challenge, the competition design, and the
resources that we provided to the participants. We provide an overview of the
top solutions, each of which use deep reinforcement learning and/or imitation
learning. We also discuss the impact of our organizational decisions on the
competition and future directions for improvement.

在 NeurIPS 2019 年会议上，我们举办了 MineRL Sample Efficient Reinforcement Learning Using Human Priors 比赛，旨在促进使用人类指导的强化学习算法的研究并减少解决复杂、分层、稀疏环境所需的样本数。本文概述了比赛的主要挑战、设计和提供给参与者的资源，并概述了使用深度强化学习和 / 或模仿学习的前几名解决方案以及我们的组织决策对比赛的影响和未来的改进方向。

关于 2019 年 MineRL 竞赛在样本高效强化学习方面的回顾分析

Retrospective Analysis of the 2019 MineRL Competition on Sample  Efficient Reinforcement Learning

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。