End-to-end approaches to autonomous driving commonly rely on expert
demonstrations. Although humans are good drivers, they are not good coaches for
end-to-end algorithms that demand dense on-policy supervision. On the contrary,
automated experts that leverage privileged information can efficiently generate
large scale on-policy and off-policy demonstrations. However, existing
automated experts for urban driving make heavy use of hand-crafted rules and
perform suboptimally even on driving simulators, where ground-truth information
is available. To address these issues, we train a reinforcement learning expert
that maps bird's-eye view images to continuous low-level actions. While setting
a new performance upper-bound on CARLA, our expert is also a better coach that
provides informative supervision signals for imitation learning agents to learn
from. Supervised by our reinforcement learning coach, a baseline end-to-end
agent with monocular camera-input achieves expert-level performance. Our
end-to-end agent achieves a 78% success rate while generalizing to a new town
and new weather on the NoCrash-dense benchmark and state-of-the-art performance
on the challenging public routes of the CARLA LeaderBoard.

通过强化学习教练，我们的端到端自主驾驶智能体实现了与人类专家水平接近的表现，其能够通过鸟瞰图像实现连续的低级别操作，提供可靠的监督信号，从而在 NoCrash-dense 基准测试和 CARLA LeaderBoard 公共路线方面达到了最先进的性能。