Here I will consider variable resolution discretization methods built in a top-down approach: an initial coarse grid is successively refined according to some splitting criterion. I will introduce and evaluate several splitting methods, from local to global approaches in which we take into account the impact of a cell on the whole state-space when deciding wether to split. I will illustrate their performance on several benchmark problems: ``Car on the Hill'', the ``Acrobot'', the ``Inverted pendulum'' and the ``Space-shuttle''. Futher research using sparse representations and Monte-Carlo methods will also be discussed. This is joint work with Andrew Moore.
In 1991, Munos graduated from the engineering school `Ecole Nationale Superieure des Telecommunications' in Paris. Following that, he pursued a diploma (DEA) in Cognitive Sciences and in Mathematics at the `University Pierre et Marie Curie', Paris. In 1997, he received a PhD from the `Ecole des Hautes Etudes en Sciences Sociales' where he worked on theoretical aspects of Reinforcement Learning in the continuous case and the link with Viscosity Solutions. Since May 1998, he has been working as a postdoctoral researcher at the Auton Lab supervised by Prof. Andrew Moore at the Robotics Institute, Carnegie Mellon University.