RLCard: A Toolkit for Reinforcement Learning in Card Games
This toolkit addresses the problem of facilitating research in reinforcement learning for imperfect information games, which is incremental as it builds on existing methods by providing new data and interfaces.
The paper introduces RLCard, an open-source toolkit for reinforcement learning research in card games, aiming to bridge reinforcement learning and imperfect information games by supporting various environments like Blackjack and Texas Hold'em, with evaluations provided.
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. The codes and documents are available at https://github.com/datamllab/rlcard