Today’s self-driving vehicles have achieved impressive driving capabilities, but still suffer from uncertain performance in long-tail cases. Training a reinforcement-learning-based self-driving algorithm with more data does not always lead to better performance, which is a safety concern. Here we present a dynamic confidence-aware reinforcement learning (DCARL) technology for guaranteed continuous improvement. Continuously improving means that more training always improves or maintains its current performance. Our technique enables performance improvement using the data collected during driving, and does not need a lengthy pre-training phase. We evaluate the proposed technology both using simulations and on an experimental vehicle. The results show that the proposed DCARL method enables continuous improvement in various cases, and, in the meantime, matches or outperforms the default self-driving policy at any stage. This technology was demonstrated and evaluated on the vehicle at the 2022 Beijing Winter Olympic Games.
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This work is supported by the National Natural Science Foundation of China (NSFC) (U1864203 (D.Y.), 52102460 (Z.C.), 61903220 (K.J.)), China Postdoctoral Science Foundation (2021M701883 (Z.C.)) and Beijing Municipal Science and Technology Commission (Z221100008122011 (D.Y.)). It is also funded by the Tsinghua University-Toyota Joint Center (D.Y.).
The authors declare no competing interests.
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Essential supplementary description for the proposed technology, detailed setting and results for the experiments, descriptions of the data file and vehicles.
Evaluation results on running self-driving vehicle.
Continuous performance improvement using confidence value.
Comparing with classical value-based RL agent.
Software to run and render the results of experiments 1 to 3.
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Cao, Z., Jiang, K., Zhou, W. et al. Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning. Nat Mach Intell 5, 145–158 (2023). https://doi.org/10.1038/s42256-023-00610-y