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» Multiagent learning using a variable learning rate
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ATAL
2008
Springer
14 years 11 months ago
Adaptive Kanerva-based function approximation for multi-agent systems
In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instanc...
Cheng Wu, Waleed Meleis
ESANN
2007
14 years 11 months ago
Agglomerative Independent Variable Group Analysis
Independent Variable Group Analysis (IVGA) is a method for grouping dependent variables together while keeping mutually independent or weakly dependent variables in separate group...
Antti Honkela, Jeremias Seppä, Esa Alhoniemi
ROBOCUP
2007
Springer
153views Robotics» more  ROBOCUP 2007»
15 years 3 months ago
Model-Based Reinforcement Learning in a Complex Domain
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
IJCNN
2006
IEEE
15 years 3 months ago
A Variational EM Approach to Predicting Uncertainty in Supervised Learning
— In many applications of supervised learning, the conditional average of the target variables is not sufficient for prediction. The dependencies between the explanatory variabl...
Markus Harva
ITS
2010
Springer
157views Multimedia» more  ITS 2010»
15 years 2 months ago
A Computational Model of Accelerated Future Learning through Feature Recognition
Accelerated future learning, in which learning proceeds more effectively and more rapidly because of prior learning, is considered to be one of the most interesting measures of ro...
Nan Li, William W. Cohen, Kenneth R. Koedinger