The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

Understand

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples.

• As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community.
• Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive.
• Resolution of these limitations requires new, sample-efficient methods.