, volume 56 , issue 2 , pages 4896-4903

Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization

Szilárd H. Tóth ^{1, 2}

Ádám Bárdos ¹

Zs.J. Viharos ^{2, 3}

Hide authors affiliations Show authors affiliations: 3 affiliations

Department of Automotive Technologies, Budapest University of Technology and Economics, Budapest, Hungary |

Research Laboratory on Engineering and Management Intelligence, Intelligent Processes Research Group Institute of Computer Science and Control, Centre of Excellence in Production Informatics and Control, Eötvös Loránd Research Network (ELKH), Centre of Excellence of the Hungarian Academy of Sciences (MTA), Budapest, Hungary |

Department of Management and Business Law, Faculty of Economics, John von Neumann University, Kecskemét, Hungary |

Publication type: Journal Article

Publication date: 2023-11-22

Elsevier

IFAC-PapersOnLine

SJR: 0.328

CiteScore: 1.8

Impact factor: —

ISSN: 24058963, 24058971

DOI: 10.1016/j.ifacol.2023.10.1261

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Industrial and Manufacturing Engineering

Environmental Engineering

Abstract

This paper aims to report on novel research results about developing a reinforcement learning agent for steady-state vehicle drift motion control. Based on the previous results of this research, the primary goal was to eliminate the problems causing learning instability experienced with the Soft Actor-Critic (SAC) algorithm applying Tabular Q-learning in this work. Trained in a MATLAB/Simulink-based simulation environment, the resulting agent succeeded in this task while being able to smoothly operate the vehicle to achieve and retain the desired target drift state, regardless of the discreet nature of the algorithm used for solving an inherently continuous task.

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GOST |

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Tóth S. H. et al. Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization // IFAC-PapersOnLine. 2023. Vol. 56. No. 2. pp. 4896-4903.

GOST all authors (up to 50) Copy

Tóth S. H., Bárdos Á., Viharos Z. Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization // IFAC-PapersOnLine. 2023. Vol. 56. No. 2. pp. 4896-4903.

RIS |

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RIS Copy

TY - JOUR

DO - 10.1016/j.ifacol.2023.10.1261

UR - https://doi.org/10.1016/j.ifacol.2023.10.1261

TI - Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization

T2 - IFAC-PapersOnLine

AU - Tóth, Szilárd H.

AU - Bárdos, Ádám

AU - Viharos, Zs.J.

PY - 2023

DA - 2023/11/22

PB - Elsevier

SP - 4896-4903

IS - 2

VL - 56

SN - 2405-8963

SN - 2405-8971

ER -

BibTex |

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BibTex (up to 50 authors) Copy

@article{2023_Tóth,

author = {Szilárd H. Tóth and Ádám Bárdos and Zs.J. Viharos},

title = {Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization},

journal = {IFAC-PapersOnLine},

year = {2023},

volume = {56},

publisher = {Elsevier},

month = {nov},

url = {https://doi.org/10.1016/j.ifacol.2023.10.1261},

number = {2},

pages = {4896--4903},

doi = {10.1016/j.ifacol.2023.10.1261}

}

MLA

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MLA Copy

Tóth, Szilárd H., et al. “Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization.” IFAC-PapersOnLine, vol. 56, no. 2, Nov. 2023, pp. 4896-4903. https://doi.org/10.1016/j.ifacol.2023.10.1261.

Publisher

Elsevier

Journal

IFAC-PapersOnLine

SJR

0.328

CiteScore

1.8

Impact factor

—

ISSN

24058963 (Print)

24058971 (Print)