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

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dc.contributor.authorHernández del Olmo, Félix
dc.contributor.authorGaudioso Vázquez, Elena
dc.contributor.authorDormido Canto, Raquel
dc.contributor.authorDuro Carralero, Natividad
dc.date.accessioned2024-05-20T11:42:50Z
dc.date.available2024-05-20T11:42:50Z
dc.date.issued2018
dc.description.abstractReinforcement 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.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.1016/j.knosys.2017.12.019
dc.identifier.issn0950-7051, eISSN 1872-7409
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12455
dc.journal.titleKnowledge-Based Systems
dc.journal.volume144
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsAtribución 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.titleTackling the start-up of a reinforcement learning agent for the control of wastewater treatment plantses
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery7c2613b6-1a06-4187-9cd1-4c51f5016c51
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