Tackling the start-up of a reinforcement learning agent for the control of wastewater treatment plants

Hernández-del-Olmo, Félix, Gaudioso, Elena, Dormido, Raquel y Duro, Natividad . (2018) Tackling the start-up of a reinforcement learning agent for the control of wastewater treatment plants. Knowledge-Based Systems, 144 (2018) 9–15

Ficheros (Some files may be inaccessible until you login with your e-spacio credentials)
Nombre Descripción Tipo MIME Size
Hernandez_del_Olmo_Felix_Tackling_the_start-u.pdf Hernandez del Olmo_Felix_Tackling the start-u.pdf application/pdf 1.06MB

Título Tackling the start-up of a reinforcement learning agent for the control of wastewater treatment plants
Autor(es) Hernández-del-Olmo, Félix
Gaudioso, Elena
Dormido, Raquel
Duro, Natividad
Materia(s) Ingeniería Informática
Abstract Reinforcement learning problems involve learning by doing. Therefore, a reinforcement learning agent will have to fail sometimes (while doing) in order to learn. Nevertheless, even with this starting error, introduced at least during the non-optimal learning stage, reinforcement learning can be affordable in some domains like the control of a wastewater treatment plant. However, in wastewater treatment plants, trying to solve the day-to-day problems, plant operators will usually not risk to leave their plant in the hands of an inexperienced and untrained reinforcement learning agent. In fact, it is somewhat obvious that plant operators will require firstly to check that the agent has been trained and that it works as it should at their particular plant. In this paper, we present a solution to this problem by giving a previous instruction to the reinforcement learning agent before we let it act on the plant. In fact, this previous instruction is the key point of the paper. In addition, this instruction is given effortlessly by the plant operator. As we will see, this solution does not just solve the starting up problem of leaving the plant in the hands of an untrained agent, but it also improves the future performance of the agent.
Editor(es) Elsevier
Fecha 2018
Formato application/pdf
Identificador bibliuned:95-Fhernandez-0007
http://e-spacio.uned.es/fez/view/bibliuned:95-Fhernandez-0007
DOI - identifier https://doi.org/10.1016/j.knosys.2017.12.019
ISSN - identifier 0950-7051, eISSN 1872-7409
Nombre de la revista Knowledge-Based Systems
Número de Volumen 144
Página inicial 9
Página final 15
Publicado en la Revista Knowledge-Based Systems, 144 (2018) 9–15
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales The registered version of this article, first published in Knowledge-Based Systems, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.knosys.2017.12.019
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Knowledge-Based Systems, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.knosys.2017.12.019

 
Versiones
Versión Tipo de filtro
Contador de citas: Google Scholar Search Google Scholar
Estadísticas de acceso: 36 Visitas, 21 Descargas  -  Estadísticas en detalle
Creado: Fri, 26 Jan 2024, 20:24:33 CET