Poker pro and software developer Nikolai Yakovenko concludes his three-part series examining how far researchers have gotten in their efforts to build a hold’em playing AI system.
In the first two parts of our consideration of the role of counter-factual regret minimization (or CFR) in the advancement of poker-related artificial intelligence, we explained how CFR works and how its implementation has helped researchers come close to “solving” heads-up limit hold’em.
To conclude the discussion, let’s delve a little more deeply into recent efforts to discover solutions for no-limit hold’em and talk about CFR’s important role in that endeavor as well.