įurther information: Stockfish (chess) and elmo (shogi engine)Ĭomparing Monte Carlo tree search searches, AlphaZero searches just 80,000 positions per second in chess and 40,000 in shogi, compared to 70 million for Stockfish and 35 million for elmo. In 2019 DeepMind published a new paper detailing MuZero, a new algorithm able to generalise AlphaZero's work, playing both Atari and board games without knowledge of the rules or representations of the game. However, the AlphaZero program itself has not been made available to the public. The trained algorithm played on a single machine with four TPUs.ĭeepMind's paper on AlphaZero was published in the journal Science on 7 December 2018. After four hours of training, DeepMind estimated AlphaZero was playing chess at a higher Elo rating than Stockfish 8 after nine hours of training, the algorithm defeated Stockfish 8 in a time-controlled 100-game tournament (28 wins, 0 losses, and 72 draws). AlphaZero was trained solely via self-play using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks, all in parallel, with no access to opening books or endgame tables. In each case it made use of custom tensor processing units (TPUs) that the Google programs were optimized to use.

On December 5, 2017, the DeepMind team released a preprint introducing AlphaZero, which within 24 hours of training achieved a superhuman level of play in these three games by defeating world-champion programs Stockfish, elmo, and the three-day version of AlphaGo Zero. This algorithm uses an approach similar to AlphaGo Zero. AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and go.

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