  One way to approach end-to-end autonomous driving is to learn a policy
function that maps from a sensory input, such as an image frame from a
front-facing camera, to a driving action, by imitating an expert driver, or a
reference policy. This can be done by supervised learning, where a policy
function is tuned to minimize the difference between the predicted and
ground-truth actions. A policy function trained in this way however is known to
suffer from unexpected behaviours due to the mismatch between the states
reachable by the reference policy and trained policy functions. More advanced
algorithms for imitation learning, such as DAgger, addresses this issue by
iteratively collecting training examples from both reference and trained
policies. These algorithms often requires a large number of queries to a
reference policy, which is undesirable as the reference policy is often
expensive. In this paper, we propose an extension of the DAgger, called
SafeDAgger, that is query-efficient and more suitable for end-to-end autonomous
driving. We evaluate the proposed SafeDAgger in a car racing simulator and show
that it indeed requires less queries to a reference policy. We observe a
significant speed up in convergence, which we conjecture to be due to the
effect of automated curriculum learning.
