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Conference Papers Year : 2010

Analysis of a Classification-based Policy Iteration Algorithm

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Alessandro Lazaric
Mohammad Ghavamzadeh
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  • PersonId : 868946
Remi Munos
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  • PersonId : 836863

Abstract

We present a classification-based policy iteration algorithm, called Direct Policy Iteration, and provide its finite-sample analysis. Our results state a performance bound in terms of the number of policy improvement steps, the number of rollouts used in each iteration, the capacity of the considered policy space, and a new capacity measure which indicates how well the policy space can approximate policies that are greedy w.r.t. any of its members. The analysis reveals a tradeoff between the estimation and approximation errors in this classification-based policy iteration setting. We also study the consistency of the method when there exists a sequence of policy spaces with increasing capacity.
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Dates and versions

inria-00482065 , version 1 (07-05-2010)
inria-00482065 , version 2 (25-01-2011)
inria-00482065 , version 3 (30-01-2012)

Identifiers

  • HAL Id : inria-00482065 , version 3

Cite

Alessandro Lazaric, Mohammad Ghavamzadeh, Remi Munos. Analysis of a Classification-based Policy Iteration Algorithm. ICML - 27th International Conference on Machine Learning, Jun 2010, Haifa, Israel. pp.607-614. ⟨inria-00482065v3⟩
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