pymahjong 1.1.2

A Japanese Mahjong environment for decision AI research.

pymahjong

A Japanese Riichi Mahjong environment for decision AI research, featuring a high-performance C++ backend with Python bindings.

Features

• Complete Rule Implementation - Full Japanese Riichi Mahjong rules including all standard yaku
• Gymnasium Compatible - Ready-to-use single-agent environment with pretrained opponents
• Multi-agent Support - 4-player environment for multi-agent research
• Oracle Observation - Access to hidden information (opponents' hands) for oracle-guided learning
• High Performance - C++ game engine with efficient Python bindings via pybind11
• Cross-platform - Pre-built wheels for Linux, macOS, and Windows (Python 3.10-3.14)

Installation

pip install pymahjong

Verify the installation:

import pymahjong
pymahjong.test()

Quick Start

Single-Agent Environment

Play against 3 opponents with a gym-like interface:

import pymahjong
import numpy as np

env = pymahjong.SingleAgentMahjongEnv(opponent_agent="random")
obs = env.reset()

while True:
    valid_actions = env.get_valid_actions()
    action = np.random.choice(valid_actions)
    obs, reward, done, _ = env.step(action)
    
    if done:
        print(f"Game over! Payoff: {reward}")
        break

Multi-Agent Environment

4 agents compete in a full game:

import pymahjong
import numpy as np

env = pymahjong.MahjongEnv()

for game in range(10):
    env.reset()
    
    while not env.is_over():
        pid = env.get_curr_player_id()
        valid_actions = env.get_valid_actions()
        obs = env.get_obs(pid)
        
        action = np.random.choice(valid_actions)
        env.step(pid, action)
    
    print(f"Game {game}: payoffs = {env.get_payoffs()}")

Documentation

Full documentation is available at https://agony5757.github.io/mahjong/

• Installation Guide
• Quick Start
• API Reference
• Advanced Topics

Pretrained Models

Pretrained opponent models are available from the GitHub releases:

env = pymahjong.SingleAgentMahjongEnv(opponent_agent="path/to/model.pth")

Offline Dataset

Human demonstration data from Tenhou.net (6 dan+ players) is available for offline RL research. Download from releases.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Citing

If you use pymahjong in your research, please cite:

@inproceedings{han2022variational,
  title     = {Variational Oracle Guiding for Reinforcement Learning},
  author    = {Dongqi Han and Tadashi Kozuno and Xufang Luo and Zhao-Yun Chen 
               and Kenji Doya and Yuqing Yang and Dongsheng Li},
  booktitle = {International Conference on Learning Representations},
  year      = {2022},
  url       = {https://openreview.net/forum?id=pjqqxepwoMy}
}

License

This project is licensed under the Apache License 2.0.

Contact

• Email: [e-mail]
• QQ Group: 608064044