1Prioritizing Point-Based POMDP Solvers

Abstract—Recent scaling up of POMDP solvers towards re-alistic applications is largely due to point-based methods that quickly converge to an approximate solution for medium-sized problems. These algorithms compute a value function for a finite reachable set of belief points, using backup operations. Point based algorithms differ on the selection of the set of belief points, and on the order by which backup operations are executed on the selected belief points. We first show how current algorithms execute a large number of backups that can be removed, without reducing the quality of the value function. We demonstrate that the ordering of backup operations on a predefined set of belief points is important. In the simpler domain of MDP solvers, prioritizing the order of equivalent backup operations on states is known to speed up convergence. We generalize the notion of prioritized backups to the POMDP framework, showing how existing algorithms can be improved by prioritizing backups. We also present a new algorithm, Prioritized Value Iteration (PVI), and show empirically that it outperforms current point-based algorithms. Finally, a new empirical evaluation measure (in addition to the standard runtime comparison), based on the number of atomic operations and the number of belief points, is proposed, in order to provide more accurate benchmark comparisons. I