• Aucun résultat trouvé

Optimism in Reinforcement Learning and Kullback-Leibler Divergence

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Optimism in Reinforcement Learning and Kullback-Leibler Divergence"

Copied!
21
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

Figure

Figure 2: SixArms Transition Model
Figure 3: Comparison of the regret of the UCRL2 and KL-UCRL algorithms in the RiverSwim environment.
Figure 5: Comparison of the regret of the UCRL2 and KL-UCRL algorithms in randomly generated sparse environments.
Figure 6: The L 1 -neighborhood { q ∈ S 3 : k p − q k 1 ≤ 0.2 } (left) and KL- KL-neighborhood { q ∈ S 3 : KL(p, q) ≤ 0.02 } (right) around the probability vector p = (0.15, 0.2, 0.65) ′ (white star)
+3

Références

Télécharger maintenant ( PDF - 21 Page - 477.12 KB )

Documents relatifs

Contrainte de correspondance Document-Document pour la RI. Application à la Divergence de Kullback-Leibler

Cette partie décrit le comportement de fonctions de correspondances de modèles populaires de Recherche d’Information pour la contrainte DDMC. Nous passons en revue le modèle

Divergence de Kullback-Leibler en grande dimension pour la classification des prairies à partir de séries temporelles d'images satellite à haute résolution

Divergence de Kullback-Leibler en grande dimension pour la classification des prairies à partir de séries temporelles d’images satellite à haute résolution.. 48èmes Journées

Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence

Value of the objective function at each iteration solving Problem (2) varying the number of iterations to solve each ND problem..A. We appreciate that setting the number of

Classification of Encrypted Internet Traffic Using Kullback Leibler Divergence and Euclidean Distance

We propose two statistical classifiers for encrypted Internet traffic based on Kullback Leibler divergence and Euclidean distance, which are computed using the flow and packet

Détection et diagnostic de défauts naissants en utilisant la divergence de Kullback-Leibler : De la théorie aux applications

The ’global’ spectral analysis approach is applied to experimen- tal vibration signals generated by different types and sizes of bearing faults, for different operating points..

High dimensional Kullback-Leibler divergence for grassland management practices classification from high resolution satellite image time series

In this study, a statistical model is proposed to identify grassland management practices using time series of a spec- tral vegetation index (NDVI) with high temporal

A new criterion based on Kullback-Leibler information for space filling designs

(1997) gave an overview of several techniques of estimation. Two methods have been selected. The first one is a Monte Carlo method where the density function is replaced with

Multivariate texture retrieval using the Kullback-Leibler divergence between bivariate generalized Gamma times an Uniform distribution

A comparative study between some multivariate models on the VisTex and Outex image database is conducted and reveals that the use of the MGΓU distribution of chromi- ance