  Solving sequential decision prediction problems, including those in imitation
learning settings, requires mitigating the problem of covariate shift. The
standard approach, DAgger, relies on capturing expert behaviour in all states
that the agent reaches. In real-world settings, querying an expert is costly.
We propose a new active learning algorithm that selectively queries the expert,
based on both a prediction of agent error and a proxy for agent risk, that
maintains the performance of unrestrained expert querying systems while
substantially reducing the number of expert queries made. We show that our
approach, RadGrad, has the potential to improve upon existing safety-aware
algorithms, and matches or exceeds the performance of DAgger and variants
(i.e., SafeDAgger) in one simulated environment. However, we also find that a
more complex environment poses challenges not only to our proposed method, but
also to existing safety-aware algorithms, which do not match the performance of
DAgger in our experiments.
