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Abstract:
Current studies have demonstrated that the representational
power of predictive state representations (PSRs) is at least
equal to the one of partially observable Markov decision
processes (POMDPs). This is while early steps in planning
and generalization with PSRs suggest substantial improvements
compared to POMDPs. However, lack of practical algorithms
for learning these representations severely restricts
their applicability. The computational inefficiency of exact
PSR learning methods naturally leads to the exploration of
various approximation methods that can provide a good set
of core tests through less computational effort. In this paper,
we address this problem in an optimization framework. In
particular, our approach aims to minimize the potential error
that may be caused by missing a number of core tests. We
provide analysis of the error caused by this compression and
present an empirical evaluation illustrating the performance
of this approach.