@InProceedings{gregoire07a,
  title =	 {Urban Traffic Control Based on Learning Agents},
  year =	 2007,
  author =	 {Pierre-Luc Gr\'{e}goire and Charles Desjardins and Julien
                  Laumonier and Brahim Chaib-draa},
  booktitle =	 {Proceedings of the 10th Internationnal {IEEE}
                  Conference on Intelligent Transportation Systems},
  abstract =	 {The optimization of traffic light control systems
                  is at the heart of work in traffic management. Many
                  of the solutions considered to design efficient
                  traffic signal patterns rely on controllers that
                  use pre-timed stages. Such systems are unable to
                  identify dynamic changes in the local traffic flow
                  and thus cannot adapt to new traffic conditions. An
                  alternative, novel approach proposed by computer
                  scientists in order to design adaptive traffic
                  light controllers relies on the use of intelligents
                  agents. The idea is to let autonomous entities,
                  named agents, learn an optimal behavior by
                  interacting directly in the system. By using
                  machine learning algorithms based on the
                  attribution of rewards according to the results of
                  the actions selected by the agents, we can obtain a
                  control policy that tries to optimize the urban
                  traffic flow. In this paper, we will explain how we
                  designed an intelligent agent that learns a traffic
                  light control policy. We will also compare this
                  policy with results from an optimal pre-timed
                  controller.},
  location =	 {Seattle, USA},
  cluster = 	 {17500220955221492287},
  url = 	 {http://jmvidal.cse.sc.edu/library/gregoire07a.pdf}
}