  Learning rational behaviors in open-world games like Minecraft remains to be
challenging for Reinforcement Learning (RL) research due to the compound
challenge of partial observability, high-dimensional visual perception and
delayed reward. To address this, we propose JueWu-MC, a sample-efficient
hierarchical RL approach equipped with representation learning and imitation
learning to deal with perception and exploration. Specifically, our approach
includes two levels of hierarchy, where the high-level controller learns a
policy to control over options and the low-level workers learn to solve each
sub-task. To boost the learning of sub-tasks, we propose a combination of
techniques including 1) action-aware representation learning which captures
underlying relations between action and representation, 2) discriminator-based
self-imitation learning for efficient exploration, and 3) ensemble behavior
cloning with consistency filtering for policy robustness. Extensive experiments
show that JueWu-MC significantly improves sample efficiency and outperforms a
set of baselines by a large margin. Notably, we won the championship of the
NeurIPS MineRL 2021 research competition and achieved the highest performance
score ever.
