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This report presents Giraffe, a chess engine that uses self-play to discoverall its domain-specific knowledge, with minimal hand-crafted knowledge given bythe programmer. Unlike previous attempts using machine learning only to performparameter-tuning on hand-crafted evaluation functions, Giraffe's learningsystem also performs automatic feature extraction and pattern recognition. Thetrained evaluation function performs comparably to the evaluation functions ofstate-of-the-art chess engines - all of which containing thousands of lines ofcarefully hand-crafted pattern recognizers, tuned over many years by bothcomputer chess experts and human chess masters. Giraffe is the most successfulattempt thus far at using end-to-end machine learning to play chess.

Giraffe: Using Deep Reinforcement Learning to Play Chess