Amine Boumaza
ABSTRACT
This article describes how racing procedures in evolution strategies can help reduce the number of evaluations. This idea is illustrated on learning Tetris players which can be addressed as a stochastic optimization problem. Different experiments show the benefits of the racing procedures in evolution strategies which can significantly reduce the number of evaluations.
- J.-Y. Audibert, R. Munos, and C. Szepesvári. Tuning bandit algorithms in stochastic environments. In M. H. et. al., editor, Algorithmic Learning Theory, volume 4754 of LNCS, pages 150--165. Springer, 2007. Google Scholar
- A. Boumaza. On the evolution of artificial tetris players. In Proceeding of the IEEE Symposium on Computational Intelligence and Games 2009 (CIG'9), pages 387--393. IEEE, June 2009. Google ScholarDigital Library
- C. P. Fahey. Tetris AI, Computer plays Tetris, 2003. On the web http://colinfahey.com/tetris/tetris_en.html.Google Scholar
- N. Hansen, S. Müller, and P. Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation, 11(1):1--18, 2003. Google ScholarDigital Library
- V. Heidrich-Meisner and C. Igel. Hoeffding and bernstein races for selecting policies in evolutionary direct policy search. In Proc. of the 26th ICML, pages 401--408, New York, 2009. ACM. Google ScholarDigital Library
- C. Thiery and B. Scherrer. Improvements on Learning Tetris with Cross Entropy. International Computer Games Association Journal, 32, 2009.Google Scholar
Index Terms
- Designing artificial tetris players with evolution strategies and racing
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