John Wiley & Sons Inc. / Neural Systems / Neural Information Processing Systems / Cambridge University Press / MIT Press / Constrained Nonlinear Stochastic Systems / D. / /
Facility
Stable Function Approximation / TU Darmstadt Plank Institute / /
IndustryTerm
model-based policy search algorithm / policy search community / analytical solutions / theoretic stochastic optimal control algorithm / closed form solution / trust-regionreflective algorithm / highlevel policy search / trivial sub-optimal solution / stochastic optimal control algorithms / closed-form solution / robotic systems / linearisation-based algorithm / linear systems / model-based policy search methods / /
Organization
Cambridge University / Department of Computer Science / MIT / TU Darmstadt Plank Institute for Intelligent Systems / /
Person
Learning Our / M. Azar / V / /
Position
The function rt / time-dependent linear feedback controller / rt / controller / high quality controller / reward function rt / /
ProgrammingLanguage
ML / /
PublishedMedium
Machine Learning / Journal of Machine Learning Research / /
Sample-Based Information-Theoretic Stochastic Optimal Control Rudolf Lioutikov1 and Alexandros Paraschos1 and Jan Peters1,2 and Gerhard Neumann1 Abstract— Many Stochastic Optimal Control (SOC) approaches rely on sample