Function Approximation |   |   | Partial Observability |   |   | Learning Methods |   |   | Ensembles |   |   |
Stochastic Optimisation |   |   | General RL |   |   | General ML |   |   | Multiagent Learning |   |   |
Comparison/Integration |   |   | Bandits |   |   | Applications |   |   | Robot Soccer |   |   |
Humanoids |   |   | Parameter |   |   | MDP |   |   | Empirical |   |   |
Failure Warning |   |   | Representation |   |   | General AI |   |   | Neural Networks |   |   |
All |   |   |
Simulation optimization using the cross-entropy method with optimal computing budget allocation
Donghai He, Loo Hay Lee, Chun-Hung Chen, Michael C. Fu, and Segev Wasserkrug, 2010
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The Knowledge-Gradient Policy for Correlated Normal Beliefs
Peter Frazier, Warren Powell, and Savas Dayanik, 2009
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Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
Verena Heidrich-Meisner and Christian Igel, 2009
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Stochastic search using the natural gradient
Yi Sun, Daan Wierstra, Tom Schaul, and Jürgen Schmidhuber, 2009
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Integrating Techniques from Statistical Ranking into Evolutionary Algorithms
Christian Schmidt, Jürgen Branke, and Stephen E. Chick, 2006
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Sequential Sampling in Noisy Environments
Jürgen Branke and Christian Schmidt, 2004
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Introduction to Stochastic Search and Optimization
James C. Spall, 2003
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Threshold selection, hypothesis tests, and DOE methods
Thomas Beielstein and Sandor Markon, 2002
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Optimization for simulation: Theory vs. Practice
Michael C. Fu, 2002
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Evolution strategies in noisy environments- a survey of existing work
D. V. Arnold, 2001
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Thresholding - a selection operator for noisy ES
Sandor Markon, Dirk V. Arnold, Thomas Bäck, Thomas Beielstein, and Hans-Georg Beyer, 2001
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Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice
Hans-Georg Beyer, 2000
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Optimization of Noisy Fitness Functions by Means of Genetic Algorithms Using History of Search
Yasuhito Sano and Hajime Kita, 2000
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Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, and Mary S. Lee, 1998
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Averaging Efficiently in the Presence of Noise
Peter Stagge, 1998
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The Racing Algorithm: Model Selection for Lazy Learners
Oded Maron and Andrew W. Moore, 1997
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Simulated Annealing for noisy cost functions
Walter J. Gutjahr and Georg Ch. Pflug, 1996
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Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise
Brad L. Miller and David E. Goldberg, 1996
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Memory-based Stochastic Optimization
Andrew W. Moore and Jeff Schneider, 1996
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Genetic algorithms in noisy environments
J. Michael Fitzpatrick and John J. Grefenstette, 1988
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