| 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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