  Imitation learning is a powerful family of techniques for learning
sensorimotor coordination in immersive environments. We apply imitation
learning to attain state-of-the-art performance on hard exploration problems in
the Minecraft environment. We report experiments that highlight the influence
of network architecture, loss function, and data augmentation. An early version
of our approach reached second place in the MineRL competition at NeurIPS 2019.
Here we report stronger results that can be used as a starting point for future
competition entries and related research. Our code is available at
https://github.com/amiranas/minerl_imitation_learning.
