Information Theoretic MPC Using Neural Network Dynamics

We introduce an information theoretic model predictive control (MPC) algorithm that is capable of controlling systems with dynamics represented by multi-layer neural networks and subject to complex cost criteria. The proposed approach is validated in two difficult simulation scenarios, a cart-pole swing up and quadrotor navigation task, and on real hardware on a 1/5th scale vehicle in an aggressive driving task.