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DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

arXiv 1701.01724 ↗First posted 6 Jan 2017cs.AIcited by 228

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Abstract (arXiv)

Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated with statistical significance professional poker players in heads-up no-limit Texas hold'em. The approach is theoretically sound and is shown to produce more difficult to exploit strategies than prior approaches.

Key facts. Cited by 228 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 45; 2022: 45; 2023: 35; 2024: 24; 2025: 43; 2026: 35.

228 papers.

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