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The game of chess is the most widely-studied domain in the history ofartificial intelligence. The strongest programs are based on a combination ofsophisticated search techniques, domain-specific adaptations, and handcraftedevaluation functions that have been refined by human experts over severaldecades. In contrast, the AlphaGo Zero program recently achieved superhumanperformance in the game of Go, by tabula rasa reinforcement learning from gamesof self-play. In this paper, we generalise this approach into a singleAlphaZero algorithm that can achieve, tabula rasa, superhuman performance inmany challenging domains. Starting from random play, and given no domainknowledge except the game rules, AlphaZero achieved within 24 hours asuperhuman level of play in the games of chess and shogi (Japanese chess) aswell as Go, and convincingly defeated a world-champion program in each case.

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm