loading
Documents

Research.Publish.Connect.

Paper

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.

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.171.183.163

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.