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Machine Learning-Robotics

Domaine
Machine Learning-Robotics
Domain - extra
Année
2014
Starting
sept. 2014
État
Open
Sujet
Deep Reinforcement Learning
Thesis advisor
SEBAG Michèle
Co-advisors
Marc Schoenauer, INRIA
Laboratory
Collaborations
Abstract
Reinforcement learning achievements critically depend on the representation of the state space. High-
dimensional state spaces (e.g. described through the many sensors or camera pixels of the robot)
hinder the characterization of the value functions. Former attempts rely on function approximations
(e.g. to deal with continuous search spaces), feature selection (to cope with high state dimensionality), or the use of models to guide the sampling of the search space.
Basically, RL involves three interdependent problems: modelling the environment and the transi-
tion model (a.k.a forward model for a robot, which can be thought of as a simulator, estimating the
next state from the current state and the selected action); modelling the environment and the reward
(a.k.a. learning the value functions, estimating how much cumulative reward the robot will get from
a given state following an improving policy); exploring the action space to support a better modelling
of transitions and val
Context
Objectives
Work program
Extra information
Prerequisite
Détails
Expected funding
Institutional funding
Status of funding
Expected
Candidates
Utilisateur
michele-martine.sebag
Créé
Jeudi 12 juin 2014 22:52:49 CEST
dernière modif.
Jeudi 12 juin 2014 22:52:49 CEST

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Ecole Doctorale Informatique Paris-Sud


Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps

ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr