Urban driver: Learning to drive from real-world demonstrations using policy gradients
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 …
policies for complex urban driving from a large corpus of real-world demonstrations. This is …
Cirl: Controllable imitative reinforcement learning for vision-based self-driving
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 …
due to the difficulty of learning an optimal driving policy. The traditional modular pipeline …
Driving policy transfer via modularity and abstraction
End-to-end approaches to autonomous driving have high sample complexity and are difficult
to scale to realistic urban driving. Simulation can help end-to-end driving systems by …
to scale to realistic urban driving. Simulation can help end-to-end driving systems by …
Learning robust control policies for end-to-end autonomous driving from data-driven simulation
A Amini, I Gilitschenski, J Phillips… - IEEE Robotics and …, 2020 - ieeexplore.ieee.org
In this work, we present a data-driven simulation and training engine capable of learning
end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging …
end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging …
Efficient reinforcement learning for autonomous driving with parameterized skills and priors
When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …
diverse driving situations. Many manually designed driving policies are difficult to scale to …
Exploring data aggregation in policy learning for vision-based urban autonomous driving
Data aggregation techniques can significantly improve vision-based policy learning within a
training environment, eg, learning to drive in a specific simulation condition. However, as on …
training environment, eg, learning to drive in a specific simulation condition. However, as on …
Model-based imitation learning for urban driving
An accurate model of the environment and the dynamic agents acting in it offers great
potential for improving motion planning. We present MILE: a Model-based Imitation …
potential for improving motion planning. We present MILE: a Model-based Imitation …
Deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety
The decision and planning system for autonomous driving in urban environments is hard to
design. Most current methods manually design the driving policy, which can be expensive to …
design. Most current methods manually design the driving policy, which can be expensive to …
CARLA: An open urban driving simulator
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA
has been developed from the ground up to support development, training, and validation of …
has been developed from the ground up to support development, training, and validation of …
Conditional affordance learning for driving in urban environments
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 …
pipelines, that build an extensive model of the environment, and imitation learning …