Information theoretic MPC for model-based reinforcement learning
Grady Williams, Nolan Wagener, Brian Goldfain, Paul Drews, James M. Rehg, Byron Boots, Evangelos A. Theodorou
Abstract
We introduce an information theoretic model predictive control (MPC) algorithm capable of handling complex cost criteria and general nonlinear dynamics. The generality of the approach makes it possible to use multi-layer neural networks as dynamics models, which we incorporate into our MPC algorithm in order to solve model-based reinforcement learning tasks. We test the algorithm in simulation on a cart-pole swing up and quadrotor navigation task, as well as on actual hardware in an aggressive driving task. Empirical results demonstrate that the algorithm is capable of achieving a high level of performance and does so only utilizing data collected from the system.
BibTeX
@inproceedings{icra2017_informationtheor,
title = {Information theoretic MPC for model-based reinforcement learning},
author = {Grady Williams and Nolan Wagener and Brian Goldfain and Paul Drews and James M. Rehg and Byron Boots and Evangelos A. Theodorou},
booktitle = {ICRA 2017},
year = {2017}
}