Simulation-based reinforcement learning for real-world autonomous driving

Understand

We use reinforcement learning in simulation to obtain a driving system controlling a full-size real-world vehicle.

• The driving policy takes RGB images from a single camera and their semantic segmentation as input.
• We use mostly synthetic data, with labelled real-world data appearing only in the training of the segmentation network.
• Using reinforcement learning in simulation and synthetic data is motivated by lowering costs and engineering effort.