@inproceedings{c054f14475c24e71b0bac3659cc546d5,
title = "Reflex-augmented reinforcement learning for electrical energy management in vehicles",
abstract = "This paper presents reflex-augmented reinforcement learning (RARL) and its application to electrical energy management in vehicles. RARL complements reinforcement learning (RL) with an organically-inspired reflex to pave the way for the application ofRL in safety-critical systems which were previously limited to rule-based decision systems. RARL also makes fast training directly in complex technical systems possible to avoid the use of a simulator. A realization ofRARL based on the cybernetic viable system model is introduced. In case of electrical energy management, RARL can be expected to outperform rule-based decision systems in terms of efficiency. This is to be shown by simulations and experiments in the real vehicle.",
keywords = "Cybernetics, Electrical energy management, Reinforcement learning, Rule-based decision system, Safety-critical system",
author = "Andreas Heimrath and Joachim Froeschl and Uwe Baumgarten",
note = "Publisher Copyright: CSREA Press {\textcopyright}.; 2018 International Conference on Artificial Intelligence, ICAI 2018 at 2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 ; Conference date: 30-07-2018 Through 02-08-2018",
year = "2018",
language = "English",
series = "2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018",
publisher = "CSREA Press",
pages = "429--430",
editor = "Arabnia, {Hamid R.} and {de la Fuente}, David and Kozerenko, {Elena B.} and Olivas, {Jose A.} and Tinetti, {Fernando G.}",
booktitle = "2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018",
}