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Closing the learning-planning loop with predictive state representations (Extended Abstract) (2010)

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by Byron Boots , Sajid M. Siddiqi , Geoffrey J. Gordon
Citations:50 - 12 self
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@MISC{Boots10closingthe,
    author = {Byron Boots and Sajid M. Siddiqi and Geoffrey J. Gordon},
    title = {Closing the learning-planning loop with predictive state representations (Extended Abstract)},
    year = {2010}
}

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Abstract

A central problem in artificial intelligence is to plan to maximize future reward under uncertainty in a partially observable environment. Models of such environments include Partially Observable Markov Decision Processes (POMDPs) [4] as well as their generalizations, Predictive State Representations (PSRs) [9] and Observable Operator Models (OOMs) [7]. POMDPs model the state of the world as a latent variable; in contrast, PSRs and OOMs represent state by tracking occurrence probabilities of a set of future events (called tests or characteristic events) conditioned on past events (called histories or indicative events). Unfortunately, exact planning algorithms such as value iteration [14] are intractable for most realistic POMDPs due to the curse of history and the curse of dimensionality [11]. However, PSRs and OOMs hold the promise of mitigating both of these curses: first, many successful approximate planning techniques designed to address

Keyphrases

general term    central problem    characteristic event    predictive state representation    future event    future reward    partially observable markov decision process    many successful approximate planning technique    indicative event    latent variable    occurrence probability    observable environment    observable operator model    past event    value iteration    realistic pomdps    artificial intelligence   

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