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Bayesian Learning of Noisy Markov Decision Processes

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  • Sumeetpal S. Singh

    (Crest)

  • Nicolas Chopin

    (Crest)

  • Nick Whiteley

    (Crest)

Abstract

This work addresses the problem of estimating the optimal value function in a MarkovDecision Process from observed state-action pairs. We adopt a Bayesian approach toinference, which allows both the model to be estimated and predictions about actions tobe made in a unified framework, providing a principled approach to mimicry of a controlleron the basis of observed data. A new Markov chain Monte Carlo (MCMC) sampler isdevised for simulation from the posterior distribution over the optimal value function.This step includes a parameter expansion step, which is shown to be essential for goodconvergence properties of the MCMC sampler. As an illustration, the method is appliedto learning a human controller.

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Bibliographic Info

Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2010-36.

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Length: 36
Date of creation: 2010
Date of revision:
Handle: RePEc:crs:wpaper:2010-36

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