A fuzzy Q-learning approach to simulate intelligent traffic control

dc.creatorPacheco, Juan C.
dc.creatorRossetti Rosaldo, J. F.
dc.creatorRodriguez, César H.
dc.date2010-01-01T08:00:00Z
dc.date.accessioned2022-10-13T13:36:05Z
dc.date.available2022-10-13T13:36:05Z
dc.identifierhttps://ciencia.lasalle.edu.co/scopus_unisalle/656
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4157629
dc.descriptionThis paper is particularly concerned with the representation of an agent behaviour suitable to model autonomous and efficient traffic control at urban network junctions. Network control efficiency is expected thus to emerge from these simpler autonomous entities cohabitating in a common environment. A fuzzy Q-learning approach is used to implement the agent-based reasoning and decision-making mechanisms and some preliminary simulation experiments are set up so as to assess the feasibility of our model to cope with urban traffic coordination.
dc.source8th International Industrial Simulation Conference 2010, ISC 2010
dc.source257
dc.subjectAgent-based modelling and simulation
dc.subjectFuzzy control
dc.subjectMarkov-Decision Process
dc.subjectMicroscopic traffic simulation
dc.titleA fuzzy Q-learning approach to simulate intelligent traffic control
dc.typeConference Proceeding


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