Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning

Matthias Bahr, Sebastian Reicherts, Philipp Sieberg, Luca Morss, Dieter Schramm

2019

Abstract

This paper deals with the application of machine learning for active roll control of motor vehicles. For this purpose, a special learning method based on reinforcement learning with an actor-critic model is used. It discusses and elaborates the basic design of the neural controller and its optimization for a fast and stable training. The methods mentioned are then validated. Both the training and the validation data are simulatively generated with the software environments MATLAB / Simulink and IPG CarMaker, while the architecture and training of the artificial neural network used is realized with the framework TensorFlow.

Paper Citation

in Harvard Style

Bahr M., Reicherts S., Sieberg P., Morss L. and Schramm D. (2019). Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning.In Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-381-0, pages 36-46. DOI: 10.5220/0007787400360046

in Bibtex Style

@conference{simultech19,
author={Matthias Bahr and Sebastian Reicherts and Philipp Sieberg and Luca Morss and Dieter Schramm},
title={Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning},
booktitle={Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2019},
pages={36-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007787400360046},
isbn={978-989-758-381-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning
SN - 978-989-758-381-0
AU - Bahr M.
AU - Reicherts S.
AU - Sieberg P.
AU - Morss L.
AU - Schramm D.
PY - 2019
SP - 36
EP - 46
DO - 10.5220/0007787400360046