FORESEE: Prediction with Expansion-Compression Unscented Transform for Online Policy Optimization

Propagating state distributions through a generic, uncertain nonlinear dynamical model is known to be intractable and usually begets numerical or analytical approximations. We introduce a method for state prediction, called the Expansion-Compression Unscented Transform, and use it to solve a class of online policy optimization problems. Our proposed algorithm propagates a finite number of sigma points through a state-dependent distribution, which dictates an increase in the number of sigma points at each time step to represent the resulting distribution; this …

algorithm class compression cs.lg cs.ro cs.sy eess.sy expansion math.oc numerical optimization policy prediction solve state through uncertain