Accepted Tutorials
TITLE: Policy Search Methods for Robotics
ABSTRACT: Policy search is a subfield in reinforcement learning which focuses on finding good parameters for a given policy parametrization. It is well suited for robotics as it can be used with efficient parametric movement representations, can cope with high-dimensional state and action spaces and deal with high-dimensional sensory inputs such as cameras. In the recent years, there has been significant progress in terms of theory and applications. Our tutorial focuses on a unified, information theoretic view on policy search methods and reviews existing algorithms in the light of this framework
SPEAKERS: Gerhard Neumann; Jan Peters
TUTORIAL WEB PAGE: Link