DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

November 27, 2017 ยท Declared Dead ยท ๐Ÿ› International Conference on Artificial Neural Networks

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Authors Eli David, Nathan S. Netanyahu, Lior Wolf arXiv ID 1711.09667 Category cs.NE: Neural & Evolutionary Cross-listed cs.LG, stat.ML Citations 88 Venue International Conference on Artificial Neural Networks Last Checked 2 months ago
Abstract
We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the more favorable one. The training relies entirely on datasets of several million chess games, and no further domain specific knowledge is incorporated. The experiments show that the resulting neural network (referred to as DeepChess) is on a par with state-of-the-art chess playing programs, which have been developed through many years of manual feature selection and tuning. DeepChess is the first end-to-end machine learning-based method that results in a grandmaster-level chess playing performance.
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