RLCard: A Toolkit for Reinforcement Learning in Card Games
Daochen Zha Affiliation: Department of Computer Science and Engineering, Texas A、M University, College Station, USA Kwei-Herng Lai Affiliation: Department of Computer Science and Engineering, Texas A、M University, College Station, USA Yuanpu Cao Thanks: Authors contribute during the visit at Texas A、M University. Affiliation: Department of Computer Science and Engineering, Texas A、M University, College Station, USA Songyi Huang Affiliation: Simon Fraser University, BC, Canada{daochen.zha, {guojunyu, Ruzhe Wei††Affiliation: Department of Computer Science and Engineering, Texas A、M University, College Station, USA Junyu Guo††Affiliation: Department of Computer Science and Engineering, Texas A、M University, College Station, USA Xia Hu Affiliation: Department of Computer Science and Engineering, Texas A、M University, College Station, USA
Abstract
We present RLCard, 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.
原文 arXiv:1910.04376;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1910.04376v2