Dissolving the Deep Learning Sample Efficiency Gap

A common observation about deep learning is that it's wildly sample inefficient compared to humans. Deep learning systems appear to need much real data or environment interaction to reach a given level of capability. A teenager can learn to drive in a few dozen hours; self-driving systems are trained for years on billions of miles of data. A human can become competitive at StarCraft II in well under a year of play, while AlphaStar required imitation learning from roughly 18 years of human games followed by 13,300 years of self-play to reach Grandmaster.