Browsing posts in: Nash Equilibrium

Counterfactual Regret Minimization – the core of Poker AI beating professional players

code in python | code in go


Last 10 years has been full of unexpected advances in artificial intelligence. Among great improvements in image processing and speech recognition – the thing that got lots of media attention was AI winning against humans in various kind of games. With OpenAI playing Dota2 and DeepMind playing Atari games in the background the most significant achievement was AlphaGo beating Korean master in Go. It was the first time machine presented super-human performance in Go marking – next to DeepBlue-Kasparov chess game in 1997 – a historical moment in the field of AI.

Around the same time a group of researchers from USA, Canada , Czech Republic and Finland had been already working on another game to solve: Heads Up No Limit Texas Hold’em

Over the years (their first papers about poker date back to 2005) researchers from University of Alberta (now in collaboration with Google Deepmind) and Carnegie Mellon University have been patiently working on advances in Game Theory with the ultimate goal to solve Poker.

Continue Reading