Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Genetically optimizing the speed of programs evolved to play tetris
Advances in genetic programming
Neuro-Dynamic Programming
Averaging Efficiently in the Presence of Noise
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Learning tetris using the noisy cross-entropy method
Neural Computation
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Tuning Bandit Algorithms in Stochastic Environments
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
On the evolution of artificial Tetris players
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Tetris is hard, even to approximate
COCOON'03 Proceedings of the 9th annual international conference on Computing and combinatorics
Integrating techniques from statistical ranking into evolutionary algorithms
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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Designing artificial players for the game of Tetris is a challenging problem that many authors addressed using different methods. Very performing implementations using evolution strategies have also been proposed. However one drawback of using evolution strategies for this problem can be the cost of evaluations due to the stochastic nature of the fitness function. This paper describes the use of racing algorithms to reduce the amount of evaluations of the fitness function in order to reduce the learning time. Different experiments illustrate the benefits and the limitation of racing in evolution strategies for this problem. Among the benefits is designing artificial players at the level of the top ranked players at a third of the cost.