[kfrzcode]

Fascinating.

> In AI, two-player zero-sum games (such as heads-up hold'em) are usually won by approximating a Nash equilibrium strategy; however, this approach does not work for games with three or more players. Pluribus instead uses an approach which lacks strong theoretical guarantees, but nevertheless appears to work well empirically at defeating human players. Across the competitions, Pluribus won an average of over 30 milli big blinds per game. Pluribus' self-learned play style eschews "limping" (calling the big blind), and engages in "donk betting" (ending a round with a call and starting the next round by betting) more often than human experts do.