Literature Database Entry


Lucas Koch, Dennis Roeser, Kevin Badalian, Alexander Lieb and Jakob Andert, "Cloud-Based Reinforcement Learning in Automotive Control Function Development," Vehicles, vol. 5 (3), pp. 914–930, August 2023.


Automotive control functions are becoming increasingly complex and their development is becoming more and more elaborate, leading to a strong need for automated solutions within the development process. Here, reinforcement learning offers a significant potential for function development to generate optimized control functions in an automated manner. Despite its successful deployment in a variety of control tasks, there is still a lack of standard tooling solutions for function development based on reinforcement learning in the automotive industry. To address this gap, we present a flexible framework that couples the conventional development process with an open-source reinforcement learning library. It features modular, physical models for relevant vehicle components, a co-simulation with a microscopic traffic simulation to generate realistic scenarios, and enables distributed and parallelized training. We demonstrate the effectiveness of our proposed method in a feasibility study to learn a control function for automated longitudinal control of an electric vehicle in an urban traffic scenario. The evolved control strategy produces a smooth trajectory with energy savings of up to 14%. The results highlight the great potential of reinforcement learning for automated control function development and prove the effectiveness of the proposed framework.

Quick access

Original Version DOI (at publishers web site)
BibTeX BibTeX


Lucas Koch
Dennis Roeser
Kevin Badalian
Alexander Lieb
Jakob Andert

BibTeX reference

    author = {Koch, Lucas and Roeser, Dennis and Badalian, Kevin and Lieb, Alexander and Andert, Jakob},
    doi = {10.3390/vehicles5030050},
    title = {{Cloud-Based Reinforcement Learning in Automotive Control Function Development}},
    pages = {914--930},
    journal = {Vehicles},
    publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
    month = {8},
    number = {3},
    volume = {5},
    year = {2023},

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at

This page was automatically generated using BibDB and bib2web.