Simulation-based reinforcement learning for real-world autonomous driving

arXiv:1911.12905v4 Announce Type: replace
Abstract: 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.
In real-world experiments we confirm that we achieved successful …

abstract arxiv autonomous autonomous driving cs.ai cs.lg cs.ro data driving images policy reinforcement reinforcement learning segmentation semantic simulation synthetic synthetic data training type world