One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better.
We present a learning algorithm that uses small amounts of human feedback to solve modern RL environments. Machine learning systems with human feedback have been explored before, but we’ve scaled up the approach to be able to work on much more complicated tasks. Our algorithm needed 900 bits of feedback from a human evaluator to learn to backflip—a seemingly simple task which is simple to judge but challenging to specify.
The overall training process is a 3-step feedback cycle between the human, the agent’s understanding of the goal, and the RL training.
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.