Query-Efficient Imitation Learning for End-to-End Autonomous Driving

Understand

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.