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. In real-
world experiments we confirm that we achieved successful sim-to-real policy transfer. Based …