I remember when I found Richard Gibson’s Ph.D. thesis from the University of Alberta. It was a massive document detailing regret minimization and strategy stitching in extensive-form games, in this case the example of three-player limit Texas hold’em. This one stands out to me because it was created at the most fundamental turn of my learning and discovery to become a poker AI developer.
At the time, I was very engrossed with my studies, and therefore had little appreciation of the nuances of game theory, and how it expresses itself in poker. Gibson’s work was very helpful. His study of Counterfactual Regret Minimization and its application to the game of poker provided me the sound theoretical basis I was in need of, at the time. His research had immense practical applications, and I was already thrilled to begin to use some of these concepts in my projects.
One night, at the end of a long day of programming and reading, I came across Gibson’s Chapter 5. He proposed novel algorithms such as Probing, Average Strategy Sampling, and Pure CFR – they sounded more like practical tools to address computation times and memory costs than theoretical novelties. It was a gold mine for someone in the position I was in, facing such limited computational resources.
His work inspired me to attempt to build some of these algorithms into my own poker bot. I can remember those nights debugging – a cup of coffee in one hand and Gibson’s dissertation in the other. Then, there was one particular night where things just clicked. My poker bot, which had been floundering, not even close to making profitable decisions, began to show signs of improvement. It was as if Gibson himself was jointly guiding my hand through all the complex subtleties of CFR and its potential applications.
It was most rewarding to test my poker bot in a small online poker tournament. I enjoyed it sailing through hands with its new found efficiency and reviewing its strategies. After all the study, programming, and sheer will, it came down to that.
It is pretty incredible how far we have come in poker AI from those times. All of the theories and algorithms that had previously only occupied the realm of academic papers have now made their way to advanced poker bots. And it all began with the inspirational works that pushed researchers like Gibson towards contributing.
Anyone interested in how the poker AI worked or how all the strategies came to be, should really check out the resources, and see what’s out there today. And if you ever get lost in the complexity, just remember, every great endeavor in AI emerges from a single line of code, and a whole bunch of curiosity.
Continue to learn, continue to program, and maybe one day your project will be one of the trend-setting projects in the world of poker AI.
Best regards 😉