Our AI agent starts by acting randomly in the environment. Periodically, two video clips of its behavior are given to a human, and the human decides which of the two clips is closest to fulfilling its goal—in this case, a backflip. The AI gradually builds a model of the goal of the task by finding the reward function that best explains the human’s judgments. It then uses RL to learn how to achieve that goal. As its behavior improves, it continues to ask for human feedback on trajectory pairs where it’s most uncertain about which is better, and further refines its understanding of the goal.

Our approach demonstrates promising sample efficiency—as stated previously, the backflip video required under 1000 bits of human feedback. It took less than an hour of a human evaluator’s time, while in the background the policy accumulated about 70 hours of overall experience (simulated at a much faster rate than real-time.) We will continue to work on reducing the amount of feedback a human needs to supply. You can see a sped-up version of the training process in the following video.