RLCard: A Toolkit for Reinforcement Learning in Card Games

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中文文档

RLCard is a toolkit for Reinforcement Learning (RL) in card games. It supports multiple card environments with easy-to-use interfaces for implementing various reinforcement learning and searching algorithms. The goal of RLCard is to bridge reinforcement learning and imperfect information games. RLCard is developed by DATA Lab at Rice and Texas A&M University, and community contributors.

• Official Website: https://www.rlcard.org
• Tutorial in Jupyter Notebook: https://github.com/datamllab/rlcard-tutorial
• Paper: https://arxiv.org/abs/1910.04376
• Video: YouTube
• GUI: RLCard-Showdown
• Dou Dizhu Demo: Demo
• Resources: Awesome-Game-AI
• Related Project: DouZero Project
• Zhihu: https://zhuanlan.zhihu.com/p/526723604
• Miscellaneous Resources:
  • Check out our open-sourced Large Time Series Model (LTSM)!
  • Have you heard of data-centric AI? Please check out our data-centric AI survey and awesome data-centric AI resources!

Community:

• Slack: Discuss in our #rlcard-project slack channel.
• QQ Group: Join our QQ group to discuss. Password: rlcardqqgroup
  • Group 1: 665647450
  • Group 2: 117349516

News:

• We have updated the tutorials in Jupyter Notebook to help you walk through RLCard! Please check RLCard Tutorial.
• All the algorithms can suppport PettingZoo now. Please check here. Thanks the contribtuion from Yifei Cheng.
• Please follow DouZero, a strong Dou Dizhu AI and the ICML 2021 paper. An online demo is available here. The algorithm is also integrated in RLCard. See Training DMC on Dou Dizhu.
• Our package is used in PettingZoo. Please check it out!
• We have released RLCard-Showdown, GUI demo for RLCard. Please check out here!
• Jupyter Notebook tutorial available! We add some examples in R to call Python interfaces of RLCard with reticulate. See here
• Thanks for the contribution of @Clarit7 for supporting different number of players in Blackjack. We call for contributions for gradually making the games more configurable. See here for more details.
• Thanks for the contribution of @Clarit7 for the Blackjack and Limit Hold'em human interface.
• Now RLCard supports environment local seeding and multiprocessing. Thanks for the testing scripts provided by @weepingwillowben.
• Human interface of NoLimit Holdem available. The action space of NoLimit Holdem has been abstracted. Thanks for the contribution of @AdrianP-.
• New game Gin Rummy and human GUI available. Thanks for the contribution of @billh0420.
• PyTorch implementation available. Thanks for the contribution of @mjudell.

Contributors

The following games are mainly developed and maintained by community contributors. Thank you!

• Gin Rummy: @billh0420
• Bridge: @billh0420

Thank all the contributors!

<a href="https://github.com/daochenzha"><img src="https://github.com/daochenzha.png" width="40px" alt="daochenzha" /></a>   <a href="https://github.com/hsywhu"><img src="https://github.com/hsywhu.png" width="40px" alt="hsywhu" /></a>   <a href="https://github.com/CaoYuanpu"><img src="https://github.com/CaoYuanpu.png" width="40px" alt="CaoYuanpu" /></a>   <a href="https://github.com/billh0420"><img src="https://github.com/billh0420.png" width="40px" alt="billh0420" /></a>   <a href="https://github.com/ruzhwei"><img src="https://github.com/ruzhwei.png" width="40px" alt="ruzhwei" /></a>   <a href="https://github.com/adrianpgob"><img src="https://github.com/adrianpgob.png" width="40px" alt="adrianpgob" /></a>   <a href="https://github.com/Zhigal"><img src="https://github.com/Zhigal.png" width="40px" alt="Zhigal" /></a>   <a href="https://github.com/aypee19"><img src="https://github.com/aypee19.png" width="40px" alt="aypee19" /></a>   <a href="https://github.com/Clarit7"><img src="https://github.com/Clarit7.png" width="40px" alt="Clarit7" /></a>   <a href="https://github.com/lhenry15"><img src="https://github.com/lhenry15.png" width="40px" alt="lhenry15" /></a>   <a href="https://github.com/ismael-elatifi"><img src="https://github.com/ismael-elatifi.png" width="40px" alt="ismael-elatifi" /></a>   <a href="https://github.com/mjudell"><img src="https://github.com/mjudell.png" width="40px" alt="mjudell" /></a>   <a href="https://github.com/jkterry1"><img src="https://github.com/jkterry1.png" width="40px" alt="jkterry1" /></a>   <a href="https://github.com/kaanozdogru"><img src="https://github.com/kaanozdogru.png" width="40px" alt="kaanozdogru" /></a>   <a href="https://github.com/junyuGuo"><img src="https://github.com/junyuGuo.png" width="40px" alt="junyuGuo" /></a>   <br /> <a href="https://github.com/Xixo99"><img src="https://github.com/Xixo99.png" width="40px" alt="Xixo99" /></a>   <a href="https://github.com/rodrigodelazcano"><img src="https://github.com/rodrigodelazcano.png" width="40px" alt="rodrigodelazcano" /></a>   <a href="https://github.com/Michael1015198808"><img src="https://github.com/Michael1015198808.png" width="40px" alt="Michael1015198808" /></a>   <a href="https://github.com/mia1996"><img src="https://github.com/mia1996.png" width="40px" alt="mia1996" /></a>   <a href="https://github.com/kaiks"><img src="https://github.com/kaiks.png" width="40px" alt="kaiks" /></a>   <a href="https://github.com/claude9493"><img src="https://github.com/claude9493.png" width="40px" alt="claude9493" /></a>   <a href="https://github.com/SonSang"><img src="https://github.com/SonSang.png" width="40px" alt="SonSang" /></a>   <a href="https://github.com/rishabhvarshney14"><img src="https://github.com/rishabhvarshney14.png" width="40px" alt="rishabhvarshney14" /></a>   <a href="https://github.com/aetheryang"><img src="https://github.com/aetheryang.png" width="40px" alt="aetheryang" /></a>   <a href="https://github.com/rxng8"><img src="https://github.com/rxng8.png" width="40px" alt="rxng8" /></a>   <a href="https://github.com/nondecidibile"><img src="https://github.com/nondecidibile.png" width="40px" alt="nondecidibile" /></a>   <a href="https://github.com/benblack769"><img src="https://github.com/benblack769.png" width="40px" alt="benblack769" /></a>   <a href="https://github.com/zhengsx"><img src="https://github.com/zhengsx.png" width="40px" alt="zhengsx" /></a>   <a href="https://github.com/andrewnc"><img src="https://github.com/andrewnc.png" width="40px" alt="andrewnc" /></a>  

Cite this work

If you find this repo useful, you may cite:

Zha, Daochen, et al. "RLCard: A Platform for Reinforcement Learning in Card Games." IJCAI. 2020.

@inproceedings{zha2020rlcard,
  title={RLCard: A Platform for Reinforcement Learning in Card Games},
  author={Zha, Daochen and Lai, Kwei-Herng and Huang, Songyi and Cao, Yuanpu and Reddy, Keerthana and Vargas, Juan and Nguyen, Alex and Wei, Ruzhe and Guo, Junyu and Hu, Xia},
  booktitle={IJCAI},
  year={2020}
}

Installation

Make sure that you have Python 3.6+ and pip installed. We recommend installing the stable version of rlcard with pip:

pip3 install rlcard

The default installation will only include the card environments. To use PyTorch implementation of the training algorithms, run

pip3 install rlcard[torch]

If you are in China and the above command is too slow, you can use the mirror provided by Tsinghua University:

pip3 install rlcard -i https://pypi.tuna.tsinghua.edu.cn/simple

Alternatively, you can clone the latest version with (if you are in China and Github is slow, you can use the mirror in Gitee):

git clone https://github.com/datamllab/rlcard.git

or only clone one branch to make it fast