LSPI with Random Projections - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Rapport (Rapport Technique) Année : 2010

LSPI with Random Projections

Mohammad Ghavamzadeh
  • Fonction : Auteur
  • PersonId : 868946
Alessandro Lazaric
Rémi Munos
  • Fonction : Auteur
  • PersonId : 836863

Résumé

We consider the problem of reinforcement learning in high-dimensional spaces when the number of features is bigger than the number of samples. In particular, we study the least-squares temporal difference (LSTD) learning algorithm when a space of low dimension is generated with a random projection from a high-dimensional space. We provide a thorough theoretical analysis of the LSTD with random projections and derive performance bounds for the resulting algorithm. We also show how the error of LSTD with random projections is propagated through the iterations of a policy iteration algorithm and provide a performance bound for the resulting least-squares policy iteration (LSPI) algorithm.

Domaines

Informatique
Fichier principal
Vignette du fichier
randproj-lspi.pdf (186.35 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

inria-00530762 , version 1 (29-10-2010)

Identifiants

  • HAL Id : inria-00530762 , version 1

Citer

Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric Maillard, Rémi Munos. LSPI with Random Projections. [Technical Report] 2010. ⟨inria-00530762⟩
209 Consultations
204 Téléchargements

Partager

Gmail Facebook X LinkedIn More