Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving - Williams, Grady; Drews, Paul; Goldfain, Brian; Rehg, James M.; Theodorou, Evangelos A. (IEEE transactions on robotics, 2018)
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 scheme to the task of aggressive autonomous driving around a dirt test track, and compare its performance with a model predictive control version of the cross-entropy method.