CLC number: TP181; U495
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-02-03
Cited: 0
Clicked: 6708
Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou. Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 673-686.
@article{title="Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving",
author="Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="5",
pages="673-686",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900637"
}
%0 Journal Article
%T Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving
%A Yunpeng Wang
%A Kunxian Zheng
%A Daxin Tian
%A Xuting Duan
%A Jianshan Zhou
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 5
%P 673-686
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900637
TY - JOUR
T1 - Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving
A1 - Yunpeng Wang
A1 - Kunxian Zheng
A1 - Daxin Tian
A1 - Xuting Duan
A1 - Jianshan Zhou
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 5
SP - 673
EP - 686
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900637
Abstract: Rule-based autonomous driving systems may suffer from increased complexity with large-scale inter-coupled rules, so many researchers are exploring learning-based approaches. reinforcement learning (RL) has been applied in designing autonomous driving systems because of its outstanding performance on a wide variety of sequential control problems. However, poor initial performance is a major challenge to the practical implementation of an RL-based autonomous driving system. RL training requires extensive training data before the model achieves reasonable performance, making an RL-based model inapplicable in a real-world setting, particularly when data are expensive. We propose an asynchronous supervised learning (ASL) method for the RL-based end-to-end autonomous driving model to address the problem of poor initial performance before training this RL-based model in real-world settings. Specifically, prior knowledge is introduced in the ASL pre-training stage by asynchronously executing multiple supervised learning processes in parallel, on multiple driving demonstration data sets. After pre-training, the model is deployed on a real vehicle to be further trained by RL to adapt to the real environment and continuously break the performance limit. The presented pre-training method is evaluated on the race car simulator, TORCS (The Open Racing Car Simulator), to verify that it can be sufficiently reliable in improving the initial performance and convergence speed of an end-to-end autonomous driving model in the RL training stage. In addition, a real-vehicle verification system is built to verify the feasibility of the proposed pre-training method in a real-vehicle deployment. Simulations results show that using some demonstrations during a supervised pre-training stage allows significant improvements in initial performance and convergence speed in the RL training stage.
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