How to Build a Superhuman Poker AI using CFR | Creating a Poker Bot Part 2

In the past few years, poker AIs have defeated the top poker players in the world. In this video, I discuss the Counterfactual Regret Minimization (CFR) algorithm that make superhuman poker bots possible. Be sure to check out Taskade, a great tool for project management, productivity, and collaboration: https://www.taskade.com

Game theory says that there is a Nash equilibrium in poker (meaning an "optimal" solution). In 2017, CMU's poker bot, Libratus, defeated 4 world-renowned poker players in heads up, at 99.98% statistical significance. In 2019, Pluribus, another CMU poker bot, defeated pros in 6-player No Limit Hold'em. The algorithm behind it all is from a domain of computer science called reinforcement learning. It is a self-play algorithm that learns the optimal strategy by playing against itself. The Counterfactual Regret Minimization (CFR) algorithm decides which decisions to make based off where it might minimize the most regret. In this video, I explain how this algorithm works!

Some of you might want to code your own poker bot. Some of you might be working on other projects. Either way, you should use Taskade! It's a great tool for managing projects and being super productive. It's also awesome for streamlining tasks and keeping track of collaboration. Best of all, it is simple to use and free!!! Check it out here: https://www.taskade.com

Timestamps:
0:00 Intro
0:56 Reinforcement Learning
2:34 Basic Idea of CFR
4:04 Game Tree and Regret
7:27 Creating Abstractions
11:38 Putting It Together
12:33 Superhuman AI Performance

Papers/Resources:
http://modelai.gettysburg.edu/2013/cf...
https://papers.nips.cc/paper/2012/fil...
https://citeseerx.ist.psu.edu/viewdoc...
https://int8.io/counterfactual-regret...
https://icml.cc/media/Slides/icml/201...

Feel free to leave any questions.

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