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

Introduction

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.

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Monte Carlo Tree Search – beginners guide

code in python code in go

For quite a long time, a common opinion in academic world was that machine achieving human master performance level in the game of Go was far from realistic. It was considered a ‘holy grail’ of AI – a milestone we were quite far away from reaching within upcoming decade. Deep Blue had its moment more than 20 years ago and since then no Go engine became close to human masters. The opinion about ‘numerical chaos’ in Go established so well it became referenced in movies, too.

Surprisingly, in march 2016 an algorithm invented by Google DeepMind called Alpha Go defeated Korean world champion in Go 4-1 proving fictional and real-life skeptics wrong. Around a year after that, Alpha Go Zero – the next generation of Alpha Go Lee (the one beating Korean master) – was reported to destroy its predecessor 100-0, being very doubtfully reachable for humans.

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