Policy-Gradients for PSRs and POMDPs

Abstract

In uncertain and partially observable environments control policies must be a function of the complete history of actions and observations. Rather than present an ever growing history to a learner, we instead track sufficient statistics of the history and map those to a control policy. The mapping has typically been done using dynamic programming, requiring large amounts of memory. We present a general approach to mapping sufficient statistics directly to control policies by combining the tracking of sufficient statistics with the use of policy-gradient reinforcement learning. The best known sufficient statistic is the belief state, computed from a known or estimated partially observable Markov decision process (POMDP) model. More recently, predictive state representations (PSRs) have emerged as a potentially compact model of partially observable systems. Our experiments explore the usefulness of both of these sufficient statistics, exact and estimated, in direct policy-search.