Metadata-Version: 2.1
Name: rlcard
Version: 0.1.2
Summary: A Toolkit for Reinforcement Learning in Card Games
Home-page: https://github.com/datamllab/rlcard
Author: Data Analytics at Texas A&M (DATA) Lab
Author-email: khlai037@tamu.edu
License: UNKNOWN
Description: # RLCard: A Toolkit for Reinforcement Learning in Card Games
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        RLCard is a toolkit for Reinforcement Learning (RL) in card games. It supports multiple card environments with easy-to-use interfaces. 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. RLCard is developed by [DATA Lab](http://faculty.cs.tamu.edu/xiahu/) at Texas A&M University.
        
        *   Official Website: [http://www.rlcard.org](http://www.rlcard.org)
        
        ## Installation
        Make sure that you have **Python 3.5+** and **pip** installed. You can install `rlcard` with `pip` as follow:
        
        ```
        git clone https://github.com/datamllab/rlcard.git
        cd rlcard
        pip install -e .
        ```
        
        ## Examples
        Please refer to [examples/](examples). A **short example** is as below.
        
        ```python
        import rlcard
        from rlcard.agents.random_agent import RandomAgent
        
        env = rlcard.make('blackjack')
        env.set_agents([RandomAgent()])
        
        trajectories, payoffs = env.run()
        ```
        
        We also recommend the following **toy examples**.
        
        *   [Playing with random agents](docs/toy-examples.md#playing-with-random-agents)
        *   [Deep-Q learning on Blackjack](docs/toy-examples.md#deep-q-learning-on-blackjack)
        *   [Running multiple processes](docs/toy-examples.md#running-multiple-processes)
        *   [Having fun with pretrained Leduc model](docs/toy-examples.md#having-fun-with-pretrained-leduc-model)
        *   [Leduc Hold'em as single-agent environment](docs/toy-examples.md#leduc-holdem-as-single-agent-environment)
        *   [Training CFR on Leduc Hold'em](docs/toy-examples.md#training-cfr-on-leduc-holdem)
        
        ## Demo
        Run `examples/leduc_holdem_human.py` to play with the pre-trained Leduc Hold'em model:
        
        ```
        >> Leduc Hold'em pre-trained model
        
        >> Start a new game!
        >> Agent 1 chooses raise
        
        =============== Community Card ===============
        ┌─────────┐
        │░░░░░░░░░│
        │░░░░░░░░░│
        │░░░░░░░░░│
        │░░░░░░░░░│
        │░░░░░░░░░│
        │░░░░░░░░░│
        │░░░░░░░░░│
        └─────────┘
        ===============   Your Hand    ===============
        ┌─────────┐
        │J        │
        │         │
        │         │
        │    ♥    │
        │         │
        │         │
        │        J│
        └─────────┘
        ===============     Chips      ===============
        Yours:   +
        Agent 1: +++
        =========== Actions You Can Choose ===========
        0: call, 1: raise, 2: fold
        
        >> You choose action (integer):
        ```
        
        ## Documents
        Please refer to the [Documents](docs/README.md) for general introductions. API documents are available at our [website](http://www.rlcard.org).
        
        ## Available Environments
        We provide a complexity estimation for the games on several aspects. **InfoSet Number:** the number of information set; **Avg. InfoSet Size:** the average number of states in a single information set; **Action Size:** the size of the action space. **Name:** the name that should be passed to `env.make` to create the game environment.
        
        | Game                                                                                                                                                                                           | InfoSet Number  | Avg. InfoSet Size | Action Size | Name            | Status     |
        | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------: | :---------------: | :---------: | :-------------: | :--------: |
        | Blackjack ([wiki](https://en.wikipedia.org/wiki/Blackjack), [baike](https://baike.baidu.com/item/21%E7%82%B9/5481683?fr=aladdin))                                                              | 10^3            | 10^1              | 10^0        | blackjack       | Available  |
        | Leduc Hold’em                                                                                                                                                                                  | 10^2            | 10^2              | 10^0        | leduc-holdem    | Available  |
        | Limit Texas Hold'em ([wiki](https://en.wikipedia.org/wiki/Texas_hold_%27em), [baike](https://baike.baidu.com/item/%E5%BE%B7%E5%85%8B%E8%90%A8%E6%96%AF%E6%89%91%E5%85%8B/83440?fr=aladdin))    | 10^14           | 10^3              | 10^0        | limit-holdem    | Available  |
        | Dou Dizhu ([wiki](https://en.wikipedia.org/wiki/Dou_dizhu), [baike](https://baike.baidu.com/item/%E6%96%97%E5%9C%B0%E4%B8%BB/177997?fr=aladdin))                                               | 10^53 ~ 10^83   | 10^23             | 10^4        | doudizhu        | Available  |
        | Mahjong ([wiki](https://en.wikipedia.org/wiki/Competition_Mahjong_scoring_rules), [baike](https://baike.baidu.com/item/%E9%BA%BB%E5%B0%86/215))                                                | 10^121          | 10^48             | 10^2        | mahjong         | Available  | 
        | No-limit Texas Hold'em ([wiki](https://en.wikipedia.org/wiki/Texas_hold_%27em), [baike](https://baike.baidu.com/item/%E5%BE%B7%E5%85%8B%E8%90%A8%E6%96%AF%E6%89%91%E5%85%8B/83440?fr=aladdin)) | 10^162          | 10^3              | 10^4        | no-limit-holdem | Available  |
        | UNO ([wiki](https://en.wikipedia.org/wiki/Uno_\(card_game), [baike](https://baike.baidu.com/item/UNO%E7%89%8C/2249587))                                                                        |  10^163         | 10^10             | 10^1        | uno             | Available  |
        | Sheng Ji ([wiki](https://en.wikipedia.org/wiki/Sheng_ji), [baike](https://baike.baidu.com/item/%E5%8D%87%E7%BA%A7/3563150))                                                                    | 10^157 ~ 10^165 | 10^61             | 10^11       | -               | Developing |
        
        ## Evaluation
        The perfomance is measured by winning rate through tournaments. Example outputs are as follows:
        ![Learning Curves](http://rlcard.org/imgs/curves.png "Learning Curves")
        
        ## Contributing
        Contribution to this project is greatly appreciated! Please create a issue for feedbacks/bugs. If you want to contribute codes, pleast contact <daochen.zha@tamu.edu> or <khlai037@tamu.edu>.
        
        ## Acknowledgements
        We would like to thank JJ World Network Technology Co.,LTD for the technical support.
        
Keywords: Reinforcement Learning,game,RL,AI
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
