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Authors: Roman Liessner ; Jakob Schmitt ; Ansgar Dietermann and Bernard Bäker

Affiliation: Dresden Institute of Automobile Engineering, TU Dresden, George-Bähr-Straße 1c, 01069 Dresden, Germany

ISBN: 978-989-758-350-6

Keyword(s): Deep Reinforcement Learning, Hyperparameter Optimization, Random Forest, Energy Management, Hybrid Electric Vehicle.

Abstract: Reinforcement Learning is a framework for algorithms that learn by interacting with an unknown environment. In recent years, combining this approach with deep learning has led to major advances in various fields. Numerous hyperparameters – e.g. the learning rate – influence the learning process and are usually determined by testing some variations. This selection strongly influences the learning result and requires a lot of time and experience. The automation of this process has the potential to make Deep Reinforcement Learning available to a wider audience and to achieve superior results. This paper presents a model-based hyperparameter optimization of the Deep Deterministic Policy Gradients (DDPG) algorithm and demonstrates it with a hybrid vehicle energy management environment. In the given case, the hyperparameter optimization is able to double the gained reward value of the DDPG agent.

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Paper citation in several formats:
Liessner, R.; Schmitt, J.; Dietermann, A. and Bäker, B. (2019). Hyperparameter Optimization for Deep Reinforcement Learning in Vehicle Energy Management.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 134-144. DOI: 10.5220/0007364701340144

@conference{icaart19,
author={Roman Liessner. and Jakob Schmitt. and Ansgar Dietermann. and Bernard Bäker.},
title={Hyperparameter Optimization for Deep Reinforcement Learning in Vehicle Energy Management},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={134-144},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007364701340144},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Hyperparameter Optimization for Deep Reinforcement Learning in Vehicle Energy Management
SN - 978-989-758-350-6
AU - Liessner, R.
AU - Schmitt, J.
AU - Dietermann, A.
AU - Bäker, B.
PY - 2019
SP - 134
EP - 144
DO - 10.5220/0007364701340144

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