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Learning Tetris Using the Noisy Cross-Entropy Method
2006
Neural Computation
The cross-entropy method is an efficient and general optimization algorithm. However, its applicability in reinforcement learning (RL) seems to be limited because it often converges to suboptimal policies. We apply noise for preventing early convergence of the cross-entropy method, using Tetris, a computer game, for demonstration. The resulting policy outperforms previous RL algorithms by almost two orders of magnitude.
doi:10.1162/neco.2006.18.12.2936
pmid:17052153
fatcat:ivlucsytgjcyfk4kf7d3p5pufq