A Fitted-Q Algorithm for Budgeted MDPs

Carrara, Nicolas; Laroche, Romain; Bouraoui, Jean-Léon; Urvoy, Tanguy; Pietquin, Olivier

Document type :

Communication dans un congrès avec actes

Title :

A Fitted-Q Algorithm for Budgeted MDPs

Author(s) :

Carrara, Nicolas [Auteur]
Orange Labs [Lannion]
Sequential Learning [SEQUEL]
Laroche, Romain [Auteur]
Maluuba
Bouraoui, Jean-Léon [Auteur]
Orange Labs [Lannion]
Urvoy, Tanguy [Auteur]
Orange Labs [Lannion]
Pietquin, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Conference title :

EWRL 2018 - 14th European workshop on Reinforcement Learning

City :

Lille

Country :

France

Start date of the conference :

2018-10-01

Publication date :

2018

English keyword(s) :

Budgeted-MDP
Fitted-Q
Reinforcement Learning

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

We address the problem of budgeted reinforcement learning, in continuous state-space, using a batch of transitions. To this extend, we introduce a novel algorithm called Budgeted Fitted-Q (BFTQ). Benchmarks show that BFTQ ...
Show more >We address the problem of budgeted reinforcement learning, in continuous state-space, using a batch of transitions. To this extend, we introduce a novel algorithm called Budgeted Fitted-Q (BFTQ). Benchmarks show that BFTQ performs as well as a regular Fitted-Q algorithm in a continuous 2-D world but also allows one to choose the right amount of budget that fits to a given task without the need of engineering the rewards. We believe that the general principles used to design BFTQ can be applied to extend others classical reinforcement learning algorithms for budgeted oriented applications.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

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A Fitted-Q Algorithm for Budgeted MDPs

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