- Clone the repo
git clone https://github.com/qq456cvb/doudizhu-C.git
- Change work directory to root
cd doudizhu-C
- Create env from environment.yml
conda env create -f environment.yml
- Activate env
conda activate doudizhu
- Build C++ files
mkdir build
cd build
cmake ..
make
- Have fun training!
cd TensorPack/MA_Hierarchical_Q
python main.py
- Download pretrained model from https://jbox.sjtu.edu.cn/l/L04d4A, then put it into
pretrained_model - 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]
- Run evaluation scripts in
scripts
cd scripts
python experiments.py
TensorPackcontain different RL algorithms to train agentsexperimentscontain scripts to evaluate agents' performance against other baselinessimulatorcontain 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.
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
@article{DBLP:journals/corr/abs-1901-08925,
author = {Yang You and
Liangwei Li and
Baisong Guo and
Weiming Wang and
Cewu Lu},
title = {Combinational Q-Learning for Dou Di Zhu},
journal = {CoRR},
volume = {abs/1901.08925},
year = {2019},
url = {http://arxiv.org/abs/1901.08925},
archivePrefix = {arXiv},
eprint = {1901.08925},
timestamp = {Sat, 02 Feb 2019 16:56:00 +0100},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-08925},
bibsource = {dblp computer science bibliography, https://dblp.org}
}