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» Variational methods for Reinforcement Learning
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ATAL
2008
Springer
13 years 9 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
TCBB
2011
13 years 2 months ago
Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification
—In biomedical data, the imbalanced data problem occurs frequently and causes poor prediction performance for minority classes. It is because the trained classifiers are mostly d...
Sangyoon Oh, Min Su Lee, Byoung-Tak Zhang
ROBOCUP
2004
Springer
147views Robotics» more  ROBOCUP 2004»
14 years 1 months ago
Learning to Drive and Simulate Autonomous Mobile Robots
We show how to apply learning methods to two robotics problems, namely the optimization of the on-board controller of an omnidirectional robot, and the derivation of a model of the...
Alexander Gloye, Cüneyt Göktekin, Anna E...
GECCO
2007
Springer
213views Optimization» more  GECCO 2007»
14 years 2 months ago
Genetically programmed learning classifier system description and results
An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the...
Gregory Anthony Harrison, Eric W. Worden
AAMAS
2010
Springer
13 years 8 months ago
Coordinated learning in multiagent MDPs with infinite state-space
Abstract In this paper we address the problem of simultaneous learning and coordination in multiagent Markov decision problems (MMDPs) with infinite state-spaces. We separate this ...
Francisco S. Melo, M. Isabel Ribeiro