  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.
