DeepStack, a deep learning algorithm, beats poker pros at NLHE
"DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker," Moravcik et al.: https://arxiv.org/abs/1701.01724
In this paper we introduce DeepStack, a new algorithm for imperfect information settings such as poker. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition about arbitrary poker situations that is automatically learned from self-play games using deep learning. In a study involving dozens of participants and 44,000 hands of poker, DeepStack becomes the first computer program to beat professional poker players in heads-up no-limit Texas
hold'em. Furthermore, we show this approach dramatically reduces worst-case exploitability compared to the abstraction paradigm that has been favored for over a decade.