Most existing approaches to autonomous driving fall into one of two categories: modular
pipelines, that build an extensive model of the environment, and imitation learning …

Hand-crafting generalised decision-making rules for real-world urban autonomous driving is
hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations is …

Deep networks trained on demonstrations of human driving have learned to follow roads
and avoid obstacles. However, driving policies trained via imitation learning cannot be …

One way to approach end-to-end autonomous driving is to learn a policy that maps from a
sensory input, such as an image frame from a front-facing camera, to a driving action, by …

Autonomous urban driving navigation with complex multi-agent dynamics is under-explored
due to the difficulty of learning an optimal driving policy. The traditional modular pipeline …

End-to-end autonomous driving provides a simple and efficient framework for autonomous
driving systems, which can directly obtain control commands from raw perception data …

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 …

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 …

In recent years, great efforts have been devoted to deep imitation learning for autonomous
driving control, where raw sensory inputs are directly mapped to control actions. However …

Reinforcement learning is considered as a promising direction for driving policy learning.
However, training autonomous driving vehicle with reinforcement learning in real …