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
Fichiers joints
filename
créé
hits
filesize
Aucun fichier joint à cette fiche
Connexion
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