Approximate Control for Continuous-Time POMDPs

This stochastic filtering approach is especially appealing for the control of such partially observed dynamical systems. This includes among others, e.g., control problems This work proposes a decision-making framework with noisy sensor measurements, such as grasping for partially observable systems in continuous and navigation in robotics (Kurniawati et al., 2008) or time with discrete state and action cognitive medium access control (Zhao et al., 2005) for spaces. As optimal decision-making becomes communication systems. For finding decision strategies, intractable for large state spaces we employ which use the available observational data to control approximation methods for the filtering and the system at hand, a solid framework can be found the control problem that scale well with an increasing in the area of optimal control (Stengel, 1994).