Texas A&M and Simon Fraser Universities Open-Source RL Toolkit for Card Games

In July the poker-playing bot Pluribus beat top professionals in a six-player no-limit Texas Hold'Em poker game. Pluribus taught itself from scratch using a form of reinforcement learning (RL) to become the first AI program to defeat elite humans in a poker game with more than two players. Compared to perfect information games such as Chess or Go, poker presents a number of unique challenges with its concealed cards, bluffing and other human strategies. Now a team of researchers from Texas A&M University and Canada's Simon Fraser University have open-sourced a toolkit called "RLCard" for applying RL research to card games. While RL has already produced a number of breakthroughs in goal-oriented tasks and has high potential, it's not without its drawbacks.