mlsanityData to 14 Oct 2026

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

arXiv 1712.01815 ↗First posted 5 Dec 2017cs.AI cs.LGcited by 734

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Cited by 102 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 734 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Dec 2017.

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Citing papers per year: 2020: 2; 2021: 96; 2022: 81; 2023: 102; 2024: 146; 2025: 190; 2026: 117.

734 papers.

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