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Apprentissage de représentation et auto-organisation modulaire pour un agent autonome

Bruno Scherrer 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This thesis studies the use of connectionist algorithms for solving reinforcement learning problems. Connectionist algorithms are inspired by the way information is processed by the brain: they rely on a large network of highly interconnected simple units, which process numerical information in a distributed and massively parallel way. Reinforcement learning is a computational theory that describes the interaction between an agent and an environment: it enables to precisely formalize goal-directed learning from interaction.

We have considered three problems, with increasing complexity, and shown that they can be solved with connectionist algorithms: 1) Reinforcement learning in a small state space: we exploit a well-known algorithm in order to build a connectionist network: the problem's paramaters are stored into weighted units and connections and the planning is the result of a distributed activity in the network. 2) Learning a representation for approximating a reinforcement learning problem with a large state space: we provide an algorithm for automatically building a state space partition in order to approximate a large problem. 3) Self-organization of specialized modules for approximating various reinforcement problems with a large state space: we exploit a ``divide and conquer'' approach and show that various tasks can efficiently be spread over a little number of specialized functional modules.
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Contributor : Bruno Scherrer <>
Submitted on : Tuesday, September 16, 2003 - 12:13:34 PM
Last modification on : Friday, February 26, 2021 - 3:28:04 PM
Long-term archiving on: : Wednesday, September 12, 2012 - 10:30:39 AM


  • HAL Id : tel-00003377, version 1


Bruno Scherrer. Apprentissage de représentation et auto-organisation modulaire pour un agent autonome. Interface homme-machine [cs.HC]. Université Henri Poincaré - Nancy I, 2003. Français. ⟨NNT : 2003NAN10018⟩. ⟨tel-00003377⟩



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