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

## TL;DR

DeepStack is an AI algorithm that combines recursive reasoning, decomposition, and deep learning to master imperfect information games like poker, defeating professional players in no-limit Texas hold'em.

## Contribution

It introduces a novel combination of recursive reasoning, decomposition, and deep learning for imperfect information games, achieving expert-level performance.

## Key findings

- DeepStack defeated professional poker players with statistical significance.
- The approach produces strategies that are more difficult to exploit than previous methods.
- It demonstrates the effectiveness of combining recursive reasoning with deep learning in complex imperfect information games.

## Abstract

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.