C++python用pybind11打王者(强化学习AI斗地主),AcceptedtoAIIDE-2020_Python.zip

# 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 ``` 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 ``` ## Evaluation against other baselines 1. Download pretrained model from https://jbox.sjtu.edu.cn/l/L04d4A or [GoogleDrive](https://drive.google.com/drive/folders/1YTNR5JYNgNfQpQ9DwhQ3ClcyeXKn_17b?usp=sharing), 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 ``` ## 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 * 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. ## DouZero 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. ## 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} } ```