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Dou Di Zhu with Combinationatorial Q-Learning

Accepted to AIIDE 2020

Step by step training tutorial

  1. Clone the repo
git clone https://github.com/qq456cvb/doudizhu-C.git
  1. Change work directory to root
cd doudizhu-C
  1. Create env from environment.yml
conda env create -f environment.yml
  1. Activate env
conda activate doudizhu
  1. Build C++ files
mkdir build
cd build
cmake ..
make
  1. Have fun training!
cd TensorPack/MA_Hierarchical_Q
python main.py

Evaluation against other baselines

  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]
  1. Run evaluation scripts in scripts
cd scripts
python experiments.py

Directory Structure

  • 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!)

Miscellaneous

References

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}
}

About

C++/python fight the lord with pybind11 (强化学习AI斗地主), Accepted to AIIDE-2020

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