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ICML
2006
IEEE
16 years 8 months ago
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (fmdps). However, these algorithms need ...
Thomas Degris, Olivier Sigaud, Pierre-Henri Wuille...
ATAL
2011
Springer
14 years 7 months ago
Metric learning for reinforcement learning agents
A key component of any reinforcement learning algorithm is the underlying representation used by the agent. While reinforcement learning (RL) agents have typically relied on hand-...
Matthew E. Taylor, Brian Kulis, Fei Sha
GECCO
2005
Springer
150views Optimization» more  GECCO 2005»
16 years 19 days ago
Population-based incremental learning with memory scheme for changing environments
In recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications. Several a...
Shengxiang Yang
CVPR
2010
IEEE
16 years 13 days ago
Cascaded L1-norm Minimization Learning (CLML) Classifier for Human Detection
This paper proposes a new learning method, which integrates feature selection with classifier construction for human detection via solving three optimization models. Firstly, the ...
Ran Xu, Baochang Zhang, Qixiang Ye, jian bin Jiao
TCS
2010
15 years 5 months ago
Active learning in heteroscedastic noise
We consider the problem of actively learning the mean values of distributions associated with a finite number of options. The decision maker can select which option to generate t...
András Antos, Varun Grover, Csaba Szepesv&a...