  We demonstrate the first large-scale application of model-based generative
adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
We augment standard MGAIL using a hierarchical model to enable generalization
to arbitrary goal routes, and measure performance using a closed-loop
evaluation framework with simulated interactive agents. We train policies from
expert trajectories collected from real vehicles driving over 100,000 miles in
San Francisco, and demonstrate a steerable policy that can navigate robustly
even in a zero-shot setting, generalizing to synthetic scenarios with novel
goals that never occurred in real-world driving. We also demonstrate the
importance of mixing closed-loop MGAIL losses with open-loop behavior cloning
losses, and show our best policy approaches the performance of the expert. We
evaluate our imitative model in both average and challenging scenarios, and
show how it can serve as a useful prior to plan successful trajectories.
