  Although deep reinforcement learning has led to breakthroughs in many
difficult domains, these successes have required an ever-increasing number of
samples, affording only a shrinking segment of the AI community access to their
development. Resolution of these limitations requires new, sample-efficient
methods. To facilitate research in this direction, we propose this second
iteration of the MineRL Competition. 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, participants
compete under a limited environment sample-complexity budget to develop systems
which solve the MineRL ObtainDiamond task in Minecraft, a sequential decision
making environment requiring long-term planning, hierarchical control, and
efficient exploration methods. The competition is structured into two rounds in
which competitors are provided several paired versions of the dataset and
environment with different game textures and shaders. At the end of each round,
competitors submit containerized versions of their learning algorithms to the
AIcrowd platform where they are trained from scratch on a hold-out
dataset-environment pair for a total of 4-days on a pre-specified hardware
platform. In this follow-up iteration to the NeurIPS 2019 MineRL Competition,
we implement new features to expand the scale and reach of the competition. In
response to the feedback of the previous participants, we introduce a second
minor track focusing on solutions without access to environment interactions of
any kind except during test-time. Further we aim to prompt domain agnostic
submissions by implementing several novel competition mechanics including
action-space randomization and desemantization of observations and actions.

Machine Learning;Artificial Intelligence;Machine Learning