  Designers of AI agents often iterate on the reward function in a
trial-and-error process until they get the desired behavior, but this only
guarantees good behavior in the training environment. We propose structuring
this process as a series of queries asking the user to compare between
different reward functions. Thus we can actively select queries for maximum
informativeness about the true reward. In contrast to approaches asking the
designer for optimal behavior, this allows us to gather additional information
by eliciting preferences between suboptimal behaviors. After each query, we
need to update the posterior over the true reward function from observing the
proxy reward function chosen by the designer. The recently proposed Inverse
Reward Design (IRD) enables this. Our approach substantially outperforms IRD in
test environments. In particular, it can query the designer about
interpretable, linear reward functions and still infer non-linear ones.
