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
@article{arxiv.1910.04376,
title = {RLCard: A Toolkit for Reinforcement Learning in Card Games},
author = {Daochen Zha and Kwei-Herng Lai and Yuanpu Cao and Songyi Huang and Ruzhe Wei and Junyu Guo and Xia Hu},
journal= {arXiv preprint arXiv:1910.04376},
year = {2020}
}
Comments
AAAI-20 Workshop on Reinforcement Learning in Games