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Authors: Kristof Van Moffaert ; Yann-Michaël De Hauwere ; Peter Vrancx and Ann Nowé

Affiliation: Vrije Universiteit Brussel, Belgium

Keyword(s): Machine Learning, Reinforcement Learning, Optimization, Multi-objective, Energy.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Formal Methods ; Industrial Applications of AI ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Planning and Scheduling ; Simulation and Modeling ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 203-209. DOI: 10.5220/0004187202030209

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Van Moffaert, K.
AU - De Hauwere, Y.
AU - Vrancx, P.
AU - Nowé, A.
PY - 2013
SP - 203
EP - 209
DO - 10.5220/0004187202030209
PB - SciTePress

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