Deep reinforcement learning for urban traffic light control

Casas, Noé. (2017). Deep reinforcement learning for urban traffic light control Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial

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Título Deep reinforcement learning for urban traffic light control
Autor(es) Casas, Noé
Abstract Traffic light timing optimization is still an active line of research despite the wealth of scientific literature on the topic, and the problem remains unsolved for any non-toy scenario. One of the key issues with traffic light optimization is the large scale of the input information that is available for the controlling agent, namely all the traffic data that is continually sampled by the traffic detectors that cover the urban network. This issue has in the past forced researchers to focus on agents that work on localized parts of the traffic network, typically on individual intersections, and to coordinate every individual agent in a multi-agent setup. In order to overcome the large scale of the available state information, we propose to rely on the ability of deep Learning approaches to handle large input spaces, in the form of Deep Deterministic Policy Gradient (DDPG) algorithm. We performed several experiments with a range of models, from the very simple one (one intersection) to the more complex one (a big city section).
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. Especialidad de Sistemas Inteligentes de Diagnóstico, Planificación y Control. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
Director/Tutor Gaudioso Vázquez, Elena
Hernández del Olmo, Félix
Fecha 2017-03-06
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Ncasas
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-Ncasas
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
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Creado: Wed, 07 Jun 2017, 20:08:50 CET