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.

RLCard is an open-source toolkit for reinforcement learning research in card games. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward. In this paper, we provide an overview of the key components in RLCard, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments.