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The MineRL competition is designed for the development of reinforcementlearning and imitation learning algorithms that can efficiently leverage humandemonstrations to drastically reduce the number of environment interactionsneeded to solve the complex \emph{ObtainDiamond} task with sparse rewards. Toaddress 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 andimitation learning. We further design a scheduler to select different agentsfor different subtasks automatically. SEIHAI takes the first place in thepreliminary and final of the NeurIPS-2020 MineRL competition.

SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition