2003 | OriginalPaper | Buchkapitel
Distributed Learning Agents in Urban Traffic Control
verfasst von : Eduardo Camponogara, Werner Kraus Jr.
Erschienen in: Progress in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Automatic learning techniques stand as promising tools to respond to the need of higher efficiency of traffic network, even more so at times of mounting pressure from economic and energy markets. To this end, this paper looks into the operation of a traffic network with distributed, intelligent agents. In particular, it casts the task of operating a traffic network as a distributed, stochastic game in which the agents solve reinforcement-learning problems. Results from computational experiments show that these agents can yield substantial gains with respect to the performance achieved by two other control policies for traffic lights. The paper ends with an outline of future research to deploy machine-learning technology in real-world traffic networks.