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

2017/12/05 by David Silver, Silver, David, Thomas Hubert +23 · 5 voices · 1,085 citations
Computer Science · Psychology · #Algorithm #Artificial Intelligence in Games #Artificial intelligence #Computer science #Psychology #Reinforcement #Reinforcement Learning in Robotics #Reinforcement learning #Social psychology #Video Analysis and Summarization #cs.AI #cs.LG

paper · pdf · doi:10.48550/arxiv.1712.01815

published in arXiv (Cornell University) (Cornell University)

arxiv created 2017/12/05 · openalex publication_date 2017/12/05 · arxiv updated 2017/12/06 · openalex created_date 2017/12/22 · openalex updated_date 2026/07/30

Abstract

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.

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• 2017 in science [wikipedia]
• AlphaGo [wikipedia]
• AlphaGo Zero [wikipedia]
• AlphaZero [wikipedia]
• CHAOS (chess) [wikipedia]
• Computer chess [wikipedia]
• Computer shogi [wikipedia]
• Dimitri Bertsekas [wikipedia]
• Elmo (shogi engine) [wikipedia]
• Glossary of artificial intelligence [wikipedia]
• Google DeepMind [wikipedia]
• Monte Carlo tree search [wikipedia]
• Move ordering [wikipedia]
• MuZero [wikipedia]
• Neural network (machine learning) [wikipedia]
• Progress in artificial intelligence [wikipedia]
• Self-play [wikipedia]
• Shogi [wikipedia]
• Stockfish (chess) [wikipedia]
• Tabula rasa [wikipedia]
• Timothy Lillicrap [wikipedia]
• Types of artificial neural networks [wikipedia]

Discussions

• Mastering Chess and Shogi by Self-Play with General Reinforcement Learning [hn, 539 points, 270 comments]
• alphazero, deep mind AI, crush chess and shogi [lobsters, 12 points, 2 comments]
• Meine Aussage bezüglich Schach-KI und neuronalen Netzen ist nicht mehr korrekt. Es muss heißen: Moderne Schach-KIs können neuronale Netze als Teil ihres Systems verwenden, aber diese Netze sind funkti [bsky, 8 points, 0 comments]
• コンピューター将棋にも黒船？Deepmindのグループがチェスと将棋にも強化学習を適用，AlphaZeroはたった2時間の学習でCSA優勝版のElmoを抜き，3日後にはすでに圧倒的な成績を収めたらしいです．今後定跡が大幅に書き変わるのか，棋譜の公開に期待． https://arxiv.org/abs/1712.01815 [bsky, 0 points, 0 comments]
• “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)” https:/ [bsky, 0 points, 0 comments]

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