Direct Policy Iteration with Demonstrations

Chemali, Jessica; Lazaric, Alessandro

Document type :

Communication dans un congrès avec actes

Title :

Direct Policy Iteration with Demonstrations

Author(s) :

Chemali, Jessica [Auteur]
Computer Science Department - Carnegie Mellon University
Lazaric, Alessandro [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]

Conference title :

IJCAI - 24th International Joint Conference on Artificial Intelligence

City :

Buenos Aires

Country :

Argentine

Start date of the conference :

2015-07-25

Publication date :

2015-07

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

We consider the problem of learning the optimal policy of an unknown Markov decision process (MDP) when expert demonstrations are available along with interaction samples. We build on classification-based policy iteration ...
Show more >We consider the problem of learning the optimal policy of an unknown Markov decision process (MDP) when expert demonstrations are available along with interaction samples. We build on classification-based policy iteration to perform a seamless integration of interaction and expert data, thus obtaining an algorithm which can benefit from both sources of information at the same time. Furthermore , we provide a full theoretical analysis of the performance across iterations providing insights on how the algorithm works. Finally, we report an empirical evaluation of the algorithm and a comparison with the state-of-the-art algorithms.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Extraction et transfert de connaissances dans l'apprentissage par renforcement

Collections :