Path Planning for Unmanned Vehicles Based on Value Function Approximation Algorithm

Jinwen Hu; Man Wang; Congzhe Zhang; Chunhui Zhao; Quan Pan; Xuemei Cheng; Feng Yang; Xiaoxu Wang

doi:10.1109/ICCA.2019.8899906

Path Planning for Unmanned Vehicles Based on Value Function Approximation Algorithm

Jinwen Hu
, Man Wang
, Congzhe Zhang
, Chunhui Zhao
, Quan Pan
, Xuemei Cheng
, Feng Yang
, Xiaoxu Wang

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper deals with the path planning problem for unmanned vehicles based on reinforcement learning. Considering the unmanned vehicles' dynamic model, the neural network is used to approximate the value function. Besides, in order to make it more suitable for practical applications and speed up the learning process, the recursive least squares algorithm is used to eliminate the inverse operation. Then some experiments are implemented to verify the effectiveness of the proposed improved value function approximation algorithm. It is proved to have improved the generalization performance of reinforcement learning in continuous space.

Original language	English
Title of host publication	2019 IEEE 15th International Conference on Control and Automation, ICCA 2019
Publisher	IEEE Computer Society
Pages	272-277
Number of pages	6
ISBN (Electronic)	9781728111643
DOIs	https://doi.org/10.1109/ICCA.2019.8899906
State	Published - Jul 2019
Event	15th IEEE International Conference on Control and Automation, ICCA 2019 - Edinburgh, United Kingdom Duration: 16 Jul 2019 → 19 Jul 2019

Publication series

Name	IEEE International Conference on Control and Automation, ICCA
Volume	2019-July
ISSN (Print)	1948-3449
ISSN (Electronic)	1948-3457

Conference

Conference	15th IEEE International Conference on Control and Automation, ICCA 2019
Country/Territory	United Kingdom
City	Edinburgh
Period	16/07/19 → 19/07/19

Access to Document

10.1109/ICCA.2019.8899906

Cite this

Hu, J., Wang, M., Zhang, C., Zhao, C., Pan, Q., Cheng, X., Yang, F., & Wang, X. (2019). Path Planning for Unmanned Vehicles Based on Value Function Approximation Algorithm. In 2019 IEEE 15th International Conference on Control and Automation, ICCA 2019 (pp. 272-277). Article 8899906 (IEEE International Conference on Control and Automation, ICCA; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICCA.2019.8899906

@inproceedings{aa54f65abb8f4835b0957a74460a351d,

title = "Path Planning for Unmanned Vehicles Based on Value Function Approximation Algorithm",

abstract = "This paper deals with the path planning problem for unmanned vehicles based on reinforcement learning. Considering the unmanned vehicles' dynamic model, the neural network is used to approximate the value function. Besides, in order to make it more suitable for practical applications and speed up the learning process, the recursive least squares algorithm is used to eliminate the inverse operation. Then some experiments are implemented to verify the effectiveness of the proposed improved value function approximation algorithm. It is proved to have improved the generalization performance of reinforcement learning in continuous space.",

author = "Jinwen Hu and Man Wang and Congzhe Zhang and Chunhui Zhao and Quan Pan and Xuemei Cheng and Feng Yang and Xiaoxu Wang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 15th IEEE International Conference on Control and Automation, ICCA 2019 ; Conference date: 16-07-2019 Through 19-07-2019",

year = "2019",

month = jul,

doi = "10.1109/ICCA.2019.8899906",

language = "英语",

series = "IEEE International Conference on Control and Automation, ICCA",

publisher = "IEEE Computer Society",

pages = "272--277",

booktitle = "2019 IEEE 15th International Conference on Control and Automation, ICCA 2019",

}

Hu, J, Wang, M, Zhang, C, Zhao, C , Pan, Q, Cheng, X, Yang, F & Wang, X 2019, Path Planning for Unmanned Vehicles Based on Value Function Approximation Algorithm. in 2019 IEEE 15th International Conference on Control and Automation, ICCA 2019., 8899906, IEEE International Conference on Control and Automation, ICCA, vol. 2019-July, IEEE Computer Society, pp. 272-277, 15th IEEE International Conference on Control and Automation, ICCA 2019, Edinburgh, United Kingdom, 16/07/19. https://doi.org/10.1109/ICCA.2019.8899906

Path Planning for Unmanned Vehicles Based on Value Function Approximation Algorithm. / Hu, Jinwen; Wang, Man; Zhang, Congzhe et al.
2019 IEEE 15th International Conference on Control and Automation, ICCA 2019. IEEE Computer Society, 2019. p. 272-277 8899906 (IEEE International Conference on Control and Automation, ICCA; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhao, Chunhui

AU - Pan, Quan

AU - Cheng, Xuemei

AU - Yang, Feng

AU - Wang, Xiaoxu

PY - 2019/7

Y1 - 2019/7

N2 - This paper deals with the path planning problem for unmanned vehicles based on reinforcement learning. Considering the unmanned vehicles' dynamic model, the neural network is used to approximate the value function. Besides, in order to make it more suitable for practical applications and speed up the learning process, the recursive least squares algorithm is used to eliminate the inverse operation. Then some experiments are implemented to verify the effectiveness of the proposed improved value function approximation algorithm. It is proved to have improved the generalization performance of reinforcement learning in continuous space.

AB - This paper deals with the path planning problem for unmanned vehicles based on reinforcement learning. Considering the unmanned vehicles' dynamic model, the neural network is used to approximate the value function. Besides, in order to make it more suitable for practical applications and speed up the learning process, the recursive least squares algorithm is used to eliminate the inverse operation. Then some experiments are implemented to verify the effectiveness of the proposed improved value function approximation algorithm. It is proved to have improved the generalization performance of reinforcement learning in continuous space.

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PB - IEEE Computer Society

T2 - 15th IEEE International Conference on Control and Automation, ICCA 2019

Y2 - 16 July 2019 through 19 July 2019

ER -

Path Planning for Unmanned Vehicles Based on Value Function Approximation Algorithm

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