Simulation-based reinforcement learning for real-world autonomous driving
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866910396526362624 |
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| author | Osiński, Błażej Jakubowski, Adam Miłoś, Piotr Zięcina, Paweł Galias, Christopher Homoceanu, Silviu Michalewski, Henryk |
| author_facet | Osiński, Błażej Jakubowski, Adam Miłoś, Piotr Zięcina, Paweł Galias, Christopher Homoceanu, Silviu Michalewski, Henryk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1911_12905 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Simulation-based reinforcement learning for real-world autonomous driving Osiński, Błażej Jakubowski, Adam Miłoś, Piotr Zięcina, Paweł Galias, Christopher Homoceanu, Silviu Michalewski, Henryk Machine Learning Artificial Intelligence Robotics 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. |
| title | Simulation-based reinforcement learning for real-world autonomous driving |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/1911.12905 |