“Giraffe: Using Deep Reinforcement Learning to Play Chess”, Matthew Lai2015-09-04 (AI chess, AlphaGo; similar)⁠:

This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer. Unlike previous attempts using machine learning only to perform parameter-tuning on hand-crafted evaluation functions, Giraffe’s learning system also performs automatic feature extraction and pattern recognition.

The trained evaluation function performs comparably to the evaluation functions of state-of-the-art chess engines—all of which contain thousands of lines of carefully hand-crafted pattern recognizers, tuned over many years by both computer chess experts and human chess masters.

Giraffe is the most successful attempt thus far at using end-to-end machine learning to play chess.

Similar Links: