InforLorV4, Main, Merge, bibRecord, 007407

Error reducing sampling in reinforcement learning

Identifieur interne : 007407 ( Main/Merge ); précédent : 007406; suivant : 007408

Error reducing sampling in reinforcement learning

Auteurs : Bruno Scherrer [France] ; Shie Mannor [États-Unis]

Source :

RBID : Hal:inria-00098352

Abstract

In reinforcement learning, an agent collects information interacting with an environment and uses it to derive a behavior. This paper focuses on efficient sampling; that is, the problem of choosing the interaction samples so that the corresponding behavior tends quickly to the optimal behavior. Our main result is a sensitivity analysis relating the choice of sampling any state-action pair to the decrease of an error bound on the optimal solution. We derive two new model-based algorithms. Simulations demonstrate a quicker convergence (in the sense of the number of samples) of the value function to the real optimal value function.

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https://hal.inria.fr/inria-00098352

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Hal:inria-00098352

Le document en format XML

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<front><div type="abstract" xml:lang="en">In reinforcement learning, an agent collects information interacting with an environment and uses it to derive a behavior. This paper focuses on efficient sampling; that is, the problem of choosing the interaction samples so that the corresponding behavior tends quickly to the optimal behavior. Our main result is a sensitivity analysis relating the choice of sampling any state-action pair to the decrease of an error bound on the optimal solution. We derive two new model-based algorithms. Simulations demonstrate a quicker convergence (in the sense of the number of samples) of the value function to the real optimal value function.</div>
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Error reducing sampling in reinforcement learning

Error reducing sampling in reinforcement learning

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Abstract

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