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» Reinforcement learning in a nutshell
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GECCO
2006
Springer
175views Optimization» more  GECCO 2006»
14 years 1 months ago
A computational theory of adaptive behavior based on an evolutionary reinforcement mechanism
Two mathematical and two computational theories from the field of human and animal learning are combined to produce a more general theory of adaptive behavior. The cornerstone of ...
J. J. McDowell, Paul L. Soto, Jesse Dallery, Saule...
NN
2006
Springer
13 years 10 months ago
Neural systems implicated in delayed and probabilistic reinforcement
This review considers the theoretical problems facing agents that must learn and choose on the basis of reward or reinforcement that is uncertain or delayed, in implicit or proced...
Rudolf N. Cardinal
ACL
2012
12 years 19 days ago
Learning High-Level Planning from Text
Comprehending action preconditions and effects is an essential step in modeling the dynamics of the world. In this paper, we express the semantics of precondition relations extrac...
S. R. K. Branavan, Nate Kushman, Tao Lei, Regina B...
ICML
2005
IEEE
14 years 11 months ago
Interactive learning of mappings from visual percepts to actions
We introduce flexible algorithms that can automatically learn mappings from images to actions by interacting with their environment. They work by introducing an image classifier i...
Justus H. Piater, Sébastien Jodogne
ATAL
2009
Springer
14 years 4 months ago
Learning of coordination: exploiting sparse interactions in multiagent systems
Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limi...
Francisco S. Melo, Manuela M. Veloso