InforLorV4, Main, Curation, bibRecord, 001B23

BRL Quasi-Optimal à l'aide de Transitions Locales Optimistes

Identifieur interne : 001B23 ( Main/Curation ); précédent : 001B22; suivant : 001B24

BRL Quasi-Optimal à l'aide de Transitions Locales Optimistes

Auteurs : Mauricio Araya [France] ; Vincent Thomas [France] ; Olivier Buffet [France]

Source :

RBID : Hal:hal-00735602

Abstract

L'apprentissage par renforcement bayésien basé modèle (BRL) permet une formalisation saine du problème consistant à agir optimalement face à un environnement inconnu, c'est-à-dire en évitant le dilemme exploration-exploitation. Toutefois, les algorithmes s'attaquant explicitement au BRL souffrent d'une telle explosion combinatoire qu'un grand nombre de travaux repose sur des algorithmes heuristiques. Cet article introduit BOLT, un algorithme heuristique simple et (presque) déterministe pour le BRL qui est optimiste vis à vis de la fonction de transition. Nous analysons la complexité d'échantillon de BOLT et montrons que, pour certains paramètres, l'algorithme est quasi-optimal au sens bayésien avec une grande probabilité. Puis, des résultats expérimentaux mettent en valeur les principales différences entre cette méthode et des travaux antérieurs.

Url:

https://hal.inria.fr/hal-00735602

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Le document en format XML

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<front><div type="abstract" xml:lang="fr">L'apprentissage par renforcement bayésien basé modèle (BRL) permet une formalisation saine du problème consistant à agir optimalement face à un environnement inconnu, c'est-à-dire en évitant le dilemme exploration-exploitation. Toutefois, les algorithmes s'attaquant explicitement au BRL souffrent d'une telle explosion combinatoire qu'un grand nombre de travaux repose sur des algorithmes heuristiques. Cet article introduit BOLT, un algorithme heuristique simple et (presque) déterministe pour le BRL qui est optimiste vis à vis de la fonction de transition. Nous analysons la complexité d'échantillon de BOLT et montrons que, pour certains paramètres, l'algorithme est quasi-optimal au sens bayésien avec une grande probabilité. Puis, des résultats expérimentaux mettent en valeur les principales différences entre cette méthode et des travaux antérieurs.</div>
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BRL Quasi-Optimal à l'aide de Transitions Locales Optimistes

BRL Quasi-Optimal à l'aide de Transitions Locales Optimistes

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