Approximate Control for Continuous-Time POMDPs

This work proposes a decision-making framework for partially observable systems in continuous time with discrete state and action spaces. As optimal decision-making becomes intractable for large state spaces we employ approximation methods for the filtering and the control problem that scale well with an increasing number of states. Specifically, we approximate the high-dimensional filtering distribution by projecting it onto a parametric family of distributions, and integrate it into a control heuristic based on the fully observable system to obtain a …

approximation continuous control cs.lg cs.sy decision distribution eess.sy filtering framework making observable q-bio.qm scale spaces state systems work