State Quantize for Pursuit Approximate Optimal Control using Reinforcement Learning

Authors: 余欢欢，买豪豪，高书林，黄喜文，羊秋玲*

Conference: ICIC 2024 Posters, Tianjin, China, August 5-8, 2024

Pages: 202-217

Keywords: approximate optimal control, pure pursuit,TD3, quantize

DOI: 10.65286/icic.v20i2.16260

Abstract

In high-speed vehicle motion scenarios, solving optimal control problems faces significant challenges in terms of time and space complexity. Ensuring real-time performance of the controller requires efficient solving algorithms and support from high-performance computing platforms. To reduce the computational cost and approach the performance of optimal control, approximate optimal control has emerged as a feasible solution. In this paper, we propose an approximate optimal vehicle control method that outperforms Model Predictive Control (MPC) in terms of performance. The method combines the pure pursuit algorithm for vehicle path tracking with the Twin Delayed DDPG (TD3) algorithm to generate approximate lookahead distance and velocity control values for the vehicle. Additionally, the vehicle state is quantized and discretized. In our experiments with a vehicle simulator, we compare the MPC control with our proposed method. The results show that while the MPC control remains stable at a vehicle speed of up to 70MPH, our method effectively controls the vehicle even at a speed of 100MPH, with higher control rate and robustness.

BibTeX Citation:

@inproceedings{ICIC2024,
    author = {余欢欢，买豪豪，高书林，黄喜文，羊秋玲* },
    title = {State Quantize for Pursuit Approximate Optimal Control using Reinforcement Learning },
    booktitle = {Proceedings of the 20th International Conference on Intelligent Computing (ICIC 2024)},
    month = {August},
    date = {5-8},
    year = {2024},
    address = {Tianjin, China},
    pages = {202-217},
    note = {Poster Volume Ⅱ}
    doi = {10.65286/icic.v20i2.16260}
}