Simulation-Based Reinforcement Learning for Real-World Autonomous Driving
Błażej Osiński, Adam Jakubowski, Paweł Zięcina, Piotr Miłoś, Christopher Galias, Silviu Homoceanu, Henryk Michalewski
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 sim-to-real policy transfer. Based on the extensive evaluation, we analyze how design decisions about perception, control, and training impact the real-world performance.
BibTeX
@inproceedings{icra2020_simulationbasedr,
title = {Simulation-Based Reinforcement Learning for Real-World Autonomous Driving},
author = {Błażej Osiński and Adam Jakubowski and Paweł Zięcina and Piotr Miłoś and Christopher Galias and Silviu Homoceanu and Henryk Michalewski},
booktitle = {ICRA 2020},
year = {2020}
}