| 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
    Details   
 A Case Study on Improving Defense Behavior in Soccer Simulation 2D: The NeuroHassle Approach
 Thomas Gabel,  Martin Riedmiller, and  Florian Trost, 2009
    Details   
 A new perspective to the keepaway soccer: the takers
 Atil Iscen and  Umut Erogul, 2008
    Details   
 On Experiences in a Complex and Competitive Gaming Domain: Reinforcement Learning Meets RoboCup
 Martin Riedmiller and  Thomas Gabel, 2007
    Details   
 Keepaway Soccer:  From Machine Learning Testbed to Benchmark
 Peter Stone,  Gregory Kuhlmann,  Matthew E. Taylor, and  Yaxin Liu, 2006
    Details   
 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
    Details