| 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 |   |   | 
 Success, strategy and skill: an experimental study
 Christopher Archibald,  Alon Altman, and  Yoav Shoham, 2010
    Details   
 SZ-Tetris as a Benchmark for Studying Key Problems of Reinforcement Learning
 István Szita and  Csaba Szepesvári, 2010
    Details   
 Improvements on Learning Tetris with Cross-Entropy
 Christophe Thierry and  Bruno Scherrer, 2010
    Details   
 Building Controllers for Tetris
 Christophe Thierry and  Bruno Scherrer, 2010
    Details   
 Modeling billiards games
 Christopher Archibald and  Yoav Shoham, 2009
    Details   
 On the Evolution of Artificial Tetris Players
 Amine Boumaza, 2009
    Details   
 Biasing Approximate Dynamic Programming with a Lower Discount Factor
 Marek Petrik and  Bruno Scherrer, 2009
    Details   
 Cross-Entropy Method for Reinforcement Learning
 Steijn Kistemaker, 2008
    Details   
 Tetris: A Study of Randomized Constraint Sampling
 Vivek F. Farias and  Benjamin Van Roy, 2006
    Details   
 Learning Tetris using the noisy cross-entropy method
 István Szita and  András L\Horincz, 2006
    Details   
 An Evolutionary Approach to Tetris
 Niko Böhm,  Gabriella Kókai, and  Stefan Mandl, 2005
    Details   
 An Adaptive Sampling Algorithm for Solving Markov Decision Processes
 Hyeong Soo Chang,  Michael C. Fu,  Jiaqiao Hu, and  Steven I. Marcus, 2005
    Details   
 Evolving a Neural Network Location Evaluator to Play Ms. Pac-Man
 Simon M. Lucas, 2005
    Details   
 Tetris is hard, even to approximate
 Ron Breukelaar,  Erik D. Demaine,  Susan Hohenberger,  Hendrik Jan Hoogeboom,  Walter A. Kosters, and  David Liben-Nowell, 2004
    Details   
 On the Numeric Stability of Gaussian Processes Regression for Relational Reinforcement Learning
 Jan Ramon and  Kurt Driessens, 2004
    Details   
 Learning to play Pac-Man: An Evolutionary, Rule-based Approach
 Marcus Gallagher and  Amanda Ryan, 2003
    Details   
 An Agent that Learns to Play Pacman
 Donald Shepherd, 2003
    Details   
 A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
 Michael Kearns,  Yishay Mansour, and  Andrew Y. Ng, 2002
    Details   
 Least-Squares Methods in Reinforcement Learning for Control
 Michail G. Lagoudakis,  Ronald Parr, and  Michael L. Littman, 2002
    Details   
 A Natural Policy Gradient
 Sham Kakade, 2001
    Details   
 How to Lose at Tetris
 Heidi Burgiel, 1997
    Details   
 Neuro-Dynamic Programming
 Dimitri P. Bertsekas and  John N. Tsitsiklis, 1996
    Details   
 Evolution-Based Discovery of Hierarchical Behaviors
 Justinian P. Rosca and  Dana H. Ballard, 1996
    Details