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Propagating Uncertainty in POMDP value iteration with Gaussian Processes AbstractIn this paper, we describe the general approach of trying to solve Partially Observable Markov Decision Processes with approximate value iteration. Methods based on this approach have shown promise for tackling larger problems where exact methods are doomed, but we explain how most of them suffer from the fundamental problem of ignoring information about the uncertainty of their estimates. We then suggest a new method for value iteration which uses Gaussian processes to form a Bayesian representation of the uncertain POMDP value function. We evaluate this method on several standard POMDPs and obtain promising results.
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