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Deep reinforcement learning, subfield of machine learning that is the basis of AlphaGo; Glossary of artificial intelligence; Go and mathematics; KataGo, the leading open-source Go program; Leela Zero, another open-source Go program; Matchbox Educable Noughts and Crosses Engine; Samuel's learning computer checkers (draughts) TD-Gammon ...
AlphaGo is a computer program developed by Google DeepMind to play the board game Go. AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. The system's neural networks were initially bootstrapped from human game-play expertise.
Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather than having some rare human-programmed edge cases to help recognize unusual Go board positions. The AI engaged in reinforcement learning, playing against itself until it could anticipate its own moves and how those moves would affect the game's outcome. [10]
AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include: [ 2 ] AZ has hard-coded rules for setting search hyperparameters .
His recent work has focused on combining reinforcement learning with deep learning, including a program that learns to play Atari games directly from pixels. [12] Silver led the AlphaGo project, culminating in the first program to defeat a top professional player in the full-size game of Go. [13]
Deep reinforcement learning reached another milestone in 2015 when AlphaGo, [16] a computer program trained with deep RL to play Go, became the first computer Go program to beat a human professional Go player without handicap on a full-sized 19×19 board.