We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are ...
Josep M. Porta, Nikos A. Vlassis, Matthijs T. J. S...
We develop a point based method for solving finitely nested interactive POMDPs approximately. Analogously to point based value iteration (PBVI) in POMDPs, we maintain a set of bel...
Partially observable Markov decision processes (POMDPs) have been
successfully applied to various robot motion planning tasks under uncertainty.
However, most existing POMDP algo...
Haoyu Bai, David Hsu, Wee Sun Lee, and Vien A. Ngo
Abstract. Current point-based planning algorithms for solving partially observable Markov decision processes (POMDPs) have demonstrated that a good approximation of the value funct...
Planning in partially-observable dynamical systems (such as POMDPs and PSRs) is a computationally challenging task. Popular approximation techniques that have proved successful ar...
Michael R. James, Michael E. Samples, Dmitri A. Do...