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NN
2010
Springer
187views Neural Networks» more  NN 2010»
14 years 10 months ago
Efficient exploration through active learning for value function approximation in reinforcement learning
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares ...
Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiya...
ECTEL
2006
Springer
15 years 7 months ago
The L2C Project: Learning to Collaborate Through Advanced SmallWorld Simulations
Abstract. L2C - Learning to Collaborate - is an ongoing research project addressing the design of effective immersive simulation-based learning experiences supporting the developme...
Albert A. Angehrn, Thierry Nabeth
ICMLA
2009
15 years 1 months ago
Automatic Feature Selection for Model-Based Reinforcement Learning in Factored MDPs
Abstract--Feature selection is an important challenge in machine learning. Unfortunately, most methods for automating feature selection are designed for supervised learning tasks a...
Mark Kroon, Shimon Whiteson
JMLR
2010
162views more  JMLR 2010»
14 years 10 months ago
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging ...
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom D...
ICSE
2008
IEEE-ACM
16 years 4 months ago
Design patterns: between programming and software design
In computer science curricula the two areas programming and software engineering are usually separated. In programming students learn an object oriented language and then deepen t...
Christoph Denzler, Dominik Gruntz