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 |   |   |
On Optimizing Interdependent Skills: A Case Study in Simulated 3D Humanoid Robot Soccer
Daniel Urieli, Patrick MacAlpine, Shivaram Kalyanakrishnan, Yinon Bentor, and Peter Stone, 2011
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A Case Study on Improving Defense Behavior in Soccer Simulation 2D: The NeuroHassle Approach
Thomas Gabel, Martin Riedmiller, and Florian Trost, 2009
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A new perspective to the keepaway soccer: the takers
Atil Iscen and Umut Erogul, 2008
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On Experiences in a Complex and Competitive Gaming Domain: Reinforcement Learning Meets RoboCup
Martin Riedmiller and Thomas Gabel, 2007
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Keepaway Soccer: From Machine Learning Testbed to Benchmark
Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, and Yaxin Liu, 2006
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Users Manual: RoboCup Soccer Server --- for Soccer Server Version 7.07 and Later
Mao Chen, Klaus Dorer, Ehsan Foroughi, Fredrick Heintz, ZhanXiang Huang, Spiros Kapetanakis, Kostas Kostiadis, Johan Kummeneje, Jan Murray, Itsuki Noda, Oliver Obst, Pat Riley, Timo Steffens, Yi Wang, and Xiang Yin, 2003
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