End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

Understand

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.