Publication: Reaching the limit in autonomous racing: Optimal control versus reinforcement learning
Reaching the limit in autonomous racing: Optimal control versus reinforcement learning
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Song, Y., Romero, A., Müller, M., Koltun, V., & Scaramuzza, D. (2023). Reaching the limit in autonomous racing: Optimal control versus reinforcement learning. Science Robotics, 8, adg1462. https://doi.org/10.1126/scirobotics.adg1462
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A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its
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- Song, Yunlong
- Romero, Angel
- Müller, Matthias
- Koltun, Vladlen
- Scaramuzza, Davide
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Song, Y., Romero, A., Müller, M., Koltun, V., & Scaramuzza, D. (2023). Reaching the limit in autonomous racing: Optimal control versus reinforcement learning. Science Robotics, 8, adg1462. https://doi.org/10.1126/scirobotics.adg1462
