  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 scalable policy. We demonstrate the
effectiveness of our approach on several partially observed systems, including
queueing systems and chemical reaction networks.
