Titre :

Optimistic planning in Markov decision processes using a generative model

Auteur(s) :

Szörényi, Balázs [Auteur]
University of Szeged [Szeged]
Sequential Learning [SEQUEL]
Kedenburg, Gunnar [Auteur]
Sequential Learning [SEQUEL]
Munos, Rémi [Auteur]
Sequential Learning [SEQUEL]

Titre de la manifestation scientifique :

Advances in Neural Information Processing Systems 27

Ville :

Montréal

Pays :

Canada

Date de début de la manifestation scientifique :

2014-12-08

Discipline(s) HAL :

Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Algorithme et structure de données [cs.DS]

Résumé en anglais : [en]

We consider the problem of online planning in a Markov decision process with discounted rewards for any given initial state. We consider the PAC sample com-plexity problem of computing, with probability 1−δ, an -optimal ...
Lire la suite >We consider the problem of online planning in a Markov decision process with discounted rewards for any given initial state. We consider the PAC sample com-plexity problem of computing, with probability 1−δ, an -optimal action using the smallest possible number of calls to the generative model (which provides reward and next-state samples). We design an algorithm, called StOP (for Stochastic-Optimistic Planning), based on the "optimism in the face of uncertainty" princi-ple. StOP can be used in the general setting, requires only a generative model, and enjoys a complexity bound that only depends on the local structure of the MDP.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL