Diamond: A MineRL Competition on Training Sample-Efficient Agents + Q&A

In the third MineRL Diamond competition, participants continue to develop algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve a complex task in Minecraft. The competition environment features sparse-rewards, long-term planning, vision and sub-task hierarchies. To ensure that truly sample-efficient are developed, organizers re-train submitted systems on a fixed cloud-computing environment for a limited number of samples (4 days or 8 million samples). To ease the entry to machine learning research, the competition features two tracks: introduction, which allows agents developed using any method ranging from end-to-end machine learning solutions to programmatic approaches; and research, which requires participants develop novel imitation and reinforcement learning algorithms to solve this difficult sample-limited task.