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arXiv · 1911.12905

Simulation-based reinforcement learning for real-world autonomous driving

Błażej Osiński·Adam Jakubowski·Piotr Miłoś·Paweł Zięcina·Christopher Galias·Silviu Homoceanu·Henryk Michalewski
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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.

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Błażej Osiński, Adam Jakubowski, Piotr Miłoś, Paweł Zięcina, Christopher Galias, Silviu Homoceanu, Henryk Michalewski. 2019-11-29. Simulation-based reinforcement learning for real-world autonomous driving. https://arxiv.org/abs/1911.12905

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