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Domain	Machine Learning-Robotics
Domain - extra
Year	2014
Starting	sept. 2014
Status	Open
Subject	Deep Reinforcement Learning
Thesis advisor	SEBAG Michèle
Co-advisors	Marc Schoenauer, INRIA
Laboratory	LRI A&O
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
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Extra information
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Details
Expected funding	Institutional funding
Status of funding	Expected
Candidates
user	michele-martine.sebag
Created	Thursday 12 of June, 2014 22:52:49 CEST
LastModif	Thursday 12 of June, 2014 22:52:49 CEST

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

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