Game-playing AI has found solutions to some versions of poker. But heads-up, no-limit Texas Hold’em represents an especially complex challenge with 10160 possible plays at different stages of the game (possibly more than the number of atoms in the universe). Such complexity exists because this two-player version of poker allows for unrestricted bet sizes. To deal with such a game, many AI rely on a technique called counterfactual regret minimization (CFR). Typical CFR algorithms try to solve games such as poker through several steps at each decision point. First, they come up with counterfactual values representing different game outcomes. Second, they apply a regret minimization approach to see which strategy leads to the best outcome. And third, they typically average the most recent strategy with all past strategies.

http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/meet-the-new-ai-challenging-human-poker-pros