Approximate Control for Continuous-Time POMDPs
This work addresses scalability challenges in decision-making for partially observable systems, which is incremental as it builds on existing approximation methods for filtering and control.
The authors tackled the intractable optimal decision-making problem for continuous-time POMDPs with large state spaces by developing an approximation framework that scales well, demonstrating its effectiveness on systems like queueing networks and chemical reaction networks.
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.