# doudizhu-C

**Repository Path**: helloxms/doudizhu-C

## Basic Information

- **Project Name**: doudizhu-C
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-30
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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, 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.
## 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

	@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}
    }