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