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» An intrinsic reward mechanism for efficient exploration
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ICML
2006
IEEE
9 years 10 months ago
An intrinsic reward mechanism for efficient exploration
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
Özgür Simsek, Andrew G. Barto
IJCNN
2008
IEEE
9 years 3 months ago
Adaptive curiosity for emotions detection in speech
— Exploratory activities seem to be crucial for our cognitive development. According to psychologists, exploration is an intrinsically rewarding behaviour. The developmental robo...
Alexis Bondu, Vincent Lemaire
BC
2002
133views more  BC 2002»
8 years 9 months ago
Cortical network reorganization guided by sensory input features
Abstract. Sensory experience alters the functional organization of cortical networks. Previous studies using behavioral training motivated by aversive or rewarding stimuli have dem...
Michael P. Kilgard, Pritesh K. Pandya, Navzer D. E...
IJCAI
2001
8 years 10 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
JSA
2010
173views more  JSA 2010»
8 years 4 months ago
Hardware/software support for adaptive work-stealing in on-chip multiprocessor
During the past few years, embedded digital systems have been requested to provide a huge amount of processing power and functionality. A very likely foreseeable step to pursue th...
Quentin L. Meunier, Frédéric P&eacut...
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