Point-Based Planning for Predictive State Representations

Abstract

Predictive state representations (PSRs) have been proposed recently as an alternative representation for environments with partial observability. The representation is rooted in actions and observations, so it holds the promise of being easier to learn than Partially Observable Markov Decision Processes (POMDPs). However, comparatively little work has explored planning algorithms using PSRs. Exact methods developed to date are no faster than existing exact planning approaches for POMDPs, and only memory-based PSRs have been shown so far to have an advantage in terms of planning speed. In this paper, we present an algorithm for approximate planning in PSRs, based on an approach similar to point-based value iteration in POMDPs. The point-based approach turns out to be a natural match for the PSR state representation. We present empirical results showing that our approach is either comparable or better than POMDP point-based planning.