On the Sample Complexity of Reinforcement Learning with a Generative Model - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

On the Sample Complexity of Reinforcement Learning with a Generative Model

Résumé

We consider the problem of learning the optimal action-value function in the discounted-reward Markov decision processes (MDPs). We prove a new PAC bound on the sample-complexity of model-based value iteration algorithm in the presence of the generative model, which indicates that for an MDP with N state-action pairs and the discount factor \gamma\in[0,1) only O(N\log(N/\delta)/((1-\gamma)^3\epsilon^2)) samples are required to find an \epsilon-optimal estimation of the action-value function with the probability 1-\delta. We also prove a matching lower bound of \Theta (N\log(N/\delta)/((1-\gamma)^3\epsilon^2)) on the sample complexity of estimating the optimal action-value function by every RL algorithm. To the best of our knowledge, this is the first matching result on the sample complexity of estimating the optimal (action-) value function in which the upper bound matches the lower bound of RL in terms of N, \epsilon, \delta and 1/(1-\gamma). Also, both our lower bound and our upper bound significantly improve on the state-of-the-art in terms of 1/(1-\gamma).
Fichier principal
Vignette du fichier
RLcomplexity.pdf (460.9 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00840331 , version 1 (02-07-2013)

Identifiants

  • HAL Id : hal-00840331 , version 1

Citer

Mohammad Gheshlaghi Azar, Rémi Munos, Hilbert Kappen. On the Sample Complexity of Reinforcement Learning with a Generative Model. International Conference on Machine Learning, 2012, United Kingdom. ⟨hal-00840331⟩
196 Consultations
171 Téléchargements

Partager

Gmail Facebook X LinkedIn More