  We present an information theoretic approach to stochastic optimal control
problems that can be used to derive general sampling based optimization
schemes. This new mathematical method is used to develop a sampling based model
predictive control algorithm. We apply this information theoretic model
predictive control (IT-MPC) scheme to the task of aggressive autonomous driving
around a dirt test track, and compare its performance to a model predictive
control version of the cross-entropy method.
