Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients

Understand

In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations.

• This is achieved by building a differentiable data-driven simulator on top of perception outputs and high-fidelity HD maps of the area.
• It allows us to synthesize new driving experiences from existing demonstrations using mid-level representations.
• Using this simulator we then train a policy network in closed-loop employing policy gradients.