Title :

Improved Exploration in Factored Average-Reward MDPs

Author(s) :

Talebi, Sadegh [Auteur]
University of Copenhagen = Københavns Universitet [UCPH]
Jonsson, Anders [Auteur]
Universitat Pompeu Fabra [Barcelona] [UPF]
Maillard, Odalric Ambrym [Auteur]
Scool [Scool]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe

Conference title :

24th International Conference on Artificial Intelligence and Statistics

City :

San diego (virtual)

Country :

Etats-Unis d'Amérique

Start date of the conference :

2021

Book title :

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics

Publication date :

2021

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]
Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

We consider a regret minimization task under the average-reward criterion in an unknown Factored Markov Decision Process (FMDP). More specifically, we consider an FMDP where the stateaction space X and the state-space S ...
Show more >We consider a regret minimization task under the average-reward criterion in an unknown Factored Markov Decision Process (FMDP). More specifically, we consider an FMDP where the stateaction space X and the state-space S admit the respective factored forms of X = ⊗ n i=1 X i and S = ⊗ m i=1 S i , and the transition and reward functions are factored over X and S. Assuming known factorization structure, we introduce a novel regret minimization strategy inspired by the popular UCRL2 strategy, called DBN-UCRL, which relies on Bernstein-type confidence sets defined for individual elements of the transition function. We show that for a generic factorization structure, DBN-UCRL achieves a regret bound, whose leading term strictly improves over existing regret bounds in terms of the dependencies on the size of S i 's and the involved diameter-related terms. We further show that when the factorization structure corresponds to the Cartesian product of some base MDPs, the regret of DBN-UCRL is upper bounded by the sum of regret of the base MDPs. We demonstrate, through numerical experiments on standard environments, that DBN-UCRL enjoys a substantially improved regret empirically over existing algorithms that have frequentist regret guarantees.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL