Apprentissage off-policy appliqué à un système de dialogue basé sur les PDMPO
Identifieur interne : 005687 ( Hal/Curation ); précédent : 005686; suivant : 005688Apprentissage off-policy appliqué à un système de dialogue basé sur les PDMPO
Auteurs : Lucie Daubigney [France] ; Matthieu Geist [France] ; Olivier Pietquin [France]Source :
Abstract
L'apprentissage par renforcement (AR) fait maintenant partie de l'état de l'art dans le domaine de l'optimisation de système de dialogues vocaux. La plupart des méthodes appliquées aux systèmes de dialogue basées sur l'AR, comme par exemple celles qui utilisent des processus gaussiens, requièrent de tester des changements plus ou moins aléatoires dans la politique. Cette manière de procéder est appelée apprentissage " onpolicy ". Néanmoins, celle-ci peut induire des comportements de la part du système incohérents aux yeux de l'utilisateur. Les algorithmes devraient idéalement trouver la politique optimale d'après l'observation d'interactions générées par une politique sous-optimale mais proposant un comportement cohérent à l'utilisateur : c'est l'apprentissage " off-policy ". Dans cette contribution, un algorithme efficace sur les échantillons permettant l'apprentissage off-policy et en ligne la politique optimale est proposé. Cet algorithme combiné à une représentation compacte, non-linéaire de la fonction de valeur (un perceptron multicouche) permet de gérer des systèmes à grande échelle.
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<front><div type="abstract" xml:lang="fr">L'apprentissage par renforcement (AR) fait maintenant partie de l'état de l'art dans le domaine de l'optimisation de système de dialogues vocaux. La plupart des méthodes appliquées aux systèmes de dialogue basées sur l'AR, comme par exemple celles qui utilisent des processus gaussiens, requièrent de tester des changements plus ou moins aléatoires dans la politique. Cette manière de procéder est appelée apprentissage " onpolicy ". Néanmoins, celle-ci peut induire des comportements de la part du système incohérents aux yeux de l'utilisateur. Les algorithmes devraient idéalement trouver la politique optimale d'après l'observation d'interactions générées par une politique sous-optimale mais proposant un comportement cohérent à l'utilisateur : c'est l'apprentissage " off-policy ". Dans cette contribution, un algorithme efficace sur les échantillons permettant l'apprentissage off-policy et en ligne la politique optimale est proposé. Cet algorithme combiné à une représentation compacte, non-linéaire de la fonction de valeur (un perceptron multicouche) permet de gérer des systèmes à grande échelle.</div>
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<abstract xml:lang="fr">L'apprentissage par renforcement (AR) fait maintenant partie de l'état de l'art dans le domaine de l'optimisation de système de dialogues vocaux. La plupart des méthodes appliquées aux systèmes de dialogue basées sur l'AR, comme par exemple celles qui utilisent des processus gaussiens, requièrent de tester des changements plus ou moins aléatoires dans la politique. Cette manière de procéder est appelée apprentissage " onpolicy ". Néanmoins, celle-ci peut induire des comportements de la part du système incohérents aux yeux de l'utilisateur. Les algorithmes devraient idéalement trouver la politique optimale d'après l'observation d'interactions générées par une politique sous-optimale mais proposant un comportement cohérent à l'utilisateur : c'est l'apprentissage " off-policy ". Dans cette contribution, un algorithme efficace sur les échantillons permettant l'apprentissage off-policy et en ligne la politique optimale est proposé. Cet algorithme combiné à une représentation compacte, non-linéaire de la fonction de valeur (un perceptron multicouche) permet de gérer des systèmes à grande échelle.</abstract>
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