Hyperparameter Optimization for Deep Reinforcement Learning in Vehicle Energy Management

Roman Liessner, Jakob Schmitt, Ansgar Dietermann, Bernard Bäker

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 Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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