DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
This addresses the longstanding problem of imperfect information in AI for games like poker, offering a theoretically sound solution with practical impact in competitive settings.
The authors tackled the challenge of imperfect information in AI by developing DeepStack, an algorithm for no-limit poker that combines recursive reasoning, decomposition, and learned intuition, which defeated professional players in a study of 44,000 hands with statistical significance.
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.