  The MineRL competition is designed for the development of reinforcement
learning and imitation learning algorithms that can efficiently leverage human
demonstrations to drastically reduce the number of environment interactions
needed to solve the complex \emph{ObtainDiamond} task with sparse rewards. To
address the challenge, in this paper, we present \textbf{SEIHAI}, a
\textbf{S}ample-\textbf{e}ff\textbf{i}cient \textbf{H}ierarchical \textbf{AI},
that fully takes advantage of the human demonstrations and the task structure.
Specifically, we split the task into several sequentially dependent subtasks,
and train a suitable agent for each subtask using reinforcement learning and
imitation learning. We further design a scheduler to select different agents
for different subtasks automatically. SEIHAI takes the first place in the
preliminary and final of the NeurIPS-2020 MineRL competition.
