1. Clone the repo

git clone https://github.com/qq456cvb/doudizhu-C.git

2. Change work directory to root

cd doudizhu-C

3. Create env from environment.yml

conda env create -f environment.yml

4. Activate env

conda activate doudizhu

5. Build C++ files

mkdir build
cd build
cmake ..
make

6. Have fun training!

cd TensorPack/MA_Hierarchical_Q
python main.py

1. Download pretrained model from https://jbox.sjtu.edu.cn/l/L04d4A or GoogleDrive, then put it into pretrained_model
2. Build Monte-Carlo baseline and move the lib into root

git clone https://github.com/qq456cvb/doudizhu-baseline.git
cd doudizhu-baseline/doudizhu
mkdir build
cd build
cmake ..
make
mv mct.cpython-36m-x86_64-linux-gnu.so [doudizhu-C ROOT]

3. Run evaluation scripts in scripts

cd scripts
python experiments.py

• TensorPack contain different RL algorithms to train agents
• experiments contain scripts to evaluate agents' performance against other baselines
• simulator contain scripts to evaluate agents' performance against online gaming platform called "QQ Dou Di Zhu" (we provide it for academic use only, use it at your own risk!)

• We provide a Monte-Carlo-Tree-Search algorithm in https://github.com/qq456cvb/doudizhu-baseline
• We provide a configured Dou Di Zhu mini-server in https://github.com/qq456cvb/doudizhu-tornado for you to play interactively. NOTE you should build the server and load pretrained model by yourself! Tutorial coming soon!
• If you meet any problems, open an issue.

Recently, another algorithm called DouZero (https://github.com/kwai/DouZero) has been proposed, to whom may be interested in a strong DouDizhu AI. It is also an actively maintained open-source project.

See our paper https://arxiv.org/pdf/1901.08925.pdf. If you find this algorithm useful or use part of its code in your projects, please consider cite

@inproceedings{you2020combinatorial,
  title={Combinatorial Q-Learning for Dou Di Zhu},
  author={You, Yang and Li, Liangwei and Guo, Baisong and Wang, Weiming and Lu, Cewu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment},
  volume={16},
  number={1},
  pages={301--307},
  year={2020}
}