  To facilitate research in the direction of sample efficient reinforcement
learning, we held the MineRL Competition on Sample Efficient Reinforcement
Learning Using Human Priors at the Thirty-third Conference on Neural
Information Processing Systems (NeurIPS 2019). The primary goal of this
competition was to promote the development of algorithms that use human
demonstrations alongside reinforcement learning to reduce the number of samples
needed to solve complex, hierarchical, and sparse environments. We describe the
competition, outlining the primary challenge, the competition design, and the
resources that we provided to the participants. We provide an overview of the
top solutions, each of which use deep reinforcement learning and/or imitation
learning. We also discuss the impact of our organizational decisions on the
competition and future directions for improvement.
