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Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem. / Kabanov, Stepan; Mitiai, German; Wu, Haitao; Petrosian, Ovanes.

Mathematical Optimization Theory and Operations Research: Recent Trends - 21st International Conference, MOTOR 2022, Revised Selected Papers. ed. / Yury Kochetov; Anton Eremeev; Oleg Khamisov; Anna Rettieva. Springer Nature, 2022. p. 338-349 (Communications in Computer and Information Science; Vol. 1661 CCIS).

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

Harvard

Kabanov, S, Mitiai, G, Wu, H & Petrosian, O 2022, Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem. in Y Kochetov, A Eremeev, O Khamisov & A Rettieva (eds), Mathematical Optimization Theory and Operations Research: Recent Trends - 21st International Conference, MOTOR 2022, Revised Selected Papers. Communications in Computer and Information Science, vol. 1661 CCIS, Springer Nature, pp. 338-349, 21st International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2022, Petrozavodsk, Russian Federation, 2/07/22. https://doi.org/10.1007/978-3-031-16224-4_24

APA

Kabanov, S., Mitiai, G., Wu, H., & Petrosian, O. (2022). Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem. In Y. Kochetov, A. Eremeev, O. Khamisov, & A. Rettieva (Eds.), Mathematical Optimization Theory and Operations Research: Recent Trends - 21st International Conference, MOTOR 2022, Revised Selected Papers (pp. 338-349). (Communications in Computer and Information Science; Vol. 1661 CCIS). Springer Nature. https://doi.org/10.1007/978-3-031-16224-4_24

Vancouver

Kabanov S, Mitiai G, Wu H, Petrosian O. Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem. In Kochetov Y, Eremeev A, Khamisov O, Rettieva A, editors, Mathematical Optimization Theory and Operations Research: Recent Trends - 21st International Conference, MOTOR 2022, Revised Selected Papers. Springer Nature. 2022. p. 338-349. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-031-16224-4_24

Author

Kabanov, Stepan ; Mitiai, German ; Wu, Haitao ; Petrosian, Ovanes. / Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem. Mathematical Optimization Theory and Operations Research: Recent Trends - 21st International Conference, MOTOR 2022, Revised Selected Papers. editor / Yury Kochetov ; Anton Eremeev ; Oleg Khamisov ; Anna Rettieva. Springer Nature, 2022. pp. 338-349 (Communications in Computer and Information Science).

BibTeX

@inproceedings{2f326b31c08f416d86169e3b43ce98c7,
title = "Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem",
abstract = "Autonomous driving systems include modules of several levels. Thanks to deep learning architectures at the moment technologies in most of the levels have high accuracy. It is important to notice that currently in autonomous driving systems for many tasks classical methods of supervised learning are no longer applicable. In this paper we are interested in a specific problem, that is to control a car to move along a given reference trajectory using reinforcement learning algorithms. In control theory, this problem is called an optimal control problem for moving along the reference trajectory. Airsim environment is used to simulate a moving car for a fixed period of time without obstacles. The purpose of our research is to determine the best reinforcement learning algorithm for a formulated problem among state-of-the-art algorithms such as DDPG, PPO, SAC, DQN and others. As a result of the conducted training and testing, it was revealed that the best algorithm for this problem is A2C.",
keywords = "Autonomous driving, Control algorithms, Optimal control of moving along the reference trajectory, Reinforcement learning",
author = "Stepan Kabanov and German Mitiai and Haitao Wu and Ovanes Petrosian",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2022 ; Conference date: 02-07-2022 Through 06-07-2022",
year = "2022",
doi = "10.1007/978-3-031-16224-4_24",
language = "English",
isbn = "9783031162237",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "338--349",
editor = "Yury Kochetov and Anton Eremeev and Oleg Khamisov and Anna Rettieva",
booktitle = "Mathematical Optimization Theory and Operations Research",
address = "Germany",

}

RIS

TY - GEN

T1 - Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem

AU - Kabanov, Stepan

AU - Mitiai, German

AU - Wu, Haitao

AU - Petrosian, Ovanes

N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - Autonomous driving systems include modules of several levels. Thanks to deep learning architectures at the moment technologies in most of the levels have high accuracy. It is important to notice that currently in autonomous driving systems for many tasks classical methods of supervised learning are no longer applicable. In this paper we are interested in a specific problem, that is to control a car to move along a given reference trajectory using reinforcement learning algorithms. In control theory, this problem is called an optimal control problem for moving along the reference trajectory. Airsim environment is used to simulate a moving car for a fixed period of time without obstacles. The purpose of our research is to determine the best reinforcement learning algorithm for a formulated problem among state-of-the-art algorithms such as DDPG, PPO, SAC, DQN and others. As a result of the conducted training and testing, it was revealed that the best algorithm for this problem is A2C.

AB - Autonomous driving systems include modules of several levels. Thanks to deep learning architectures at the moment technologies in most of the levels have high accuracy. It is important to notice that currently in autonomous driving systems for many tasks classical methods of supervised learning are no longer applicable. In this paper we are interested in a specific problem, that is to control a car to move along a given reference trajectory using reinforcement learning algorithms. In control theory, this problem is called an optimal control problem for moving along the reference trajectory. Airsim environment is used to simulate a moving car for a fixed period of time without obstacles. The purpose of our research is to determine the best reinforcement learning algorithm for a formulated problem among state-of-the-art algorithms such as DDPG, PPO, SAC, DQN and others. As a result of the conducted training and testing, it was revealed that the best algorithm for this problem is A2C.

KW - Autonomous driving

KW - Control algorithms

KW - Optimal control of moving along the reference trajectory

KW - Reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=85140465648&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/64117091-4c1b-3689-81f8-e1f6a155b52b/

U2 - 10.1007/978-3-031-16224-4_24

DO - 10.1007/978-3-031-16224-4_24

M3 - Conference contribution

AN - SCOPUS:85140465648

SN - 9783031162237

T3 - Communications in Computer and Information Science

SP - 338

EP - 349

BT - Mathematical Optimization Theory and Operations Research

A2 - Kochetov, Yury

A2 - Eremeev, Anton

A2 - Khamisov, Oleg

A2 - Rettieva, Anna

PB - Springer Nature

T2 - 21st International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2022

Y2 - 2 July 2022 through 6 July 2022

ER -

ID: 101415278