Reinforcement Learning for Multi-purpose Schedules

Kristof Van Moffaert, Yann-Michaël De Hauwere, Peter Vrancx, Ann Nowé

2013

Abstract

In this paper, we present a learning technique for determining schedules for general devices that focus on a combination of two objectives. These objectives are user-convenience and gains in energy savings. The proposed learning algorithm is based on Fitted-Q Iteration (FQI) and analyzes the usage and the users of a particular device to decide upon the appropriate profile of start-up and shutdown times of that equipment. The algorithm is experimentally evaluated on real-life data to discover that close-to-optimal control policies can be learned on a short timespan of a only few iterations. Our results show that the algorithm is capable of proposing intelligent schedules depending on which objective the user placed more or less emphasis on.

Paper Citation

in Harvard Style

Van Moffaert K., De Hauwere Y., Vrancx P. and Nowé A. (2013). Reinforcement Learning for Multi-purpose Schedules . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 203-209. DOI: 10.5220/0004187202030209

in Bibtex Style

@conference{icaart13,
author={Kristof Van Moffaert and Yann-Michaël De Hauwere and Peter Vrancx and Ann Nowé},
title={Reinforcement Learning for Multi-purpose Schedules},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={203-209},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004187202030209},
isbn={978-989-8565-39-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Reinforcement Learning for Multi-purpose Schedules
SN - 978-989-8565-39-6
AU - Van Moffaert K.
AU - De Hauwere Y.
AU - Vrancx P.
AU - Nowé A.
PY - 2013
SP - 203
EP - 209
DO - 10.5220/0004187202030209