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Authors: Nassim Haddam 1 ; Benjamin Cohen Boulakia 1 and Dominique Barth 2

Affiliations: 1 LINEACT - Recherche & Innovation, CESI, Nanterre, France ; 2 DAVID Laboratory, Paris-Saclay - UVSQ, Versailles, France

Keyword(s): Smart Building, Reinforcement Learning, Light Control, Light Comfort.

Abstract: In the context of smart building energy management, we address in this work the problem of controlling light so as to minimise energy usage of the building while maintaining the satisfaction of the user regarding comfort using a stateless Reinforcement Learning approach. We consider that the user can freely interact with the building and changes the intensity of the light according to his comfort. Moreover, we consider that the behaviour of the user depends not only on present conditions but also on past behaviour of the control system. In this setting, we use the pursuit algorithm to control the signal and investigate the impact of the discretization of the action space on the convergence speed of the algorithm and the quality of the policies learned by the agent. We propose ways to accelerate convergence speed by varying the maximal duration of the actions while maintaining the quality of the policies and compare different solutions to achieve it.

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Paper citation in several formats:
Haddam, N.; Boulakia, B. and Barth, D. (2022). Accelerated Variant of Reinforcement Learning Algorithms for Light Control with Non-stationary User Behaviour. In Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS; ISBN 978-989-758-572-2; ISSN 2184-4968, SciTePress, pages 78-85. DOI: 10.5220/0010987900003203

@conference{smartgreens22,
author={Nassim Haddam. and Benjamin Cohen Boulakia. and Dominique Barth.},
title={Accelerated Variant of Reinforcement Learning Algorithms for Light Control with Non-stationary User Behaviour},
booktitle={Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS},
year={2022},
pages={78-85},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010987900003203},
isbn={978-989-758-572-2},
issn={2184-4968},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS
TI - Accelerated Variant of Reinforcement Learning Algorithms for Light Control with Non-stationary User Behaviour
SN - 978-989-758-572-2
IS - 2184-4968
AU - Haddam, N.
AU - Boulakia, B.
AU - Barth, D.
PY - 2022
SP - 78
EP - 85
DO - 10.5220/0010987900003203
PB - SciTePress

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