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Learning Tetris using the noisy cross-entropy method
1szityu@eotvos.elte.hu
Neural Computation
|October 21, 2006
Abstract:
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.
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