# cczero2 from Github

**Repository Path**: EthanLifeGreat/cczero2

## Basic Information

- **Project Name**: cczero2 from Github
- **Description**: 从Github 搬运的中国象棋人工智能程序，棋力水平约为天天象棋业9-1。
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 2
- **Created**: 2024-02-13
- **Last Updated**: 2025-02-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: 人工智能, 强化学习

## README

这是一个从[Github](https://github.com/xtxiatian200/cczero2)克隆来的中国象棋Zero人工智能机器人，据称象棋水平达到业9-1，本人对GUI作了一点改动。

以下是原readme








# 中国象棋Zero（CCZero）

## About

Chinese Chess reinforcement learning by [AlphaZero](https://arxiv.org/abs/1712.01815) methods.

This project is based on these main resources:
1. DeepMind's Oct 19th publication: [Mastering the Game of Go without Human Knowledge](https://www.nature.com/articles/nature24270.epdf?author_access_token=VJXbVjaSHxFoctQQ4p2k4tRgN0jAjWel9jnR3ZoTv0PVW4gB86EEpGqTRDtpIz-2rmo8-KG06gqVobU5NSCFeHILHcVFUeMsbvwS-lxjqQGg98faovwjxeTUgZAUMnRQ).
2. The **great** Reversi/Chess/Chinese chess development of the DeepMind ideas that @mokemokechicken/@Akababa/@TDteach did in their repo: https://github.com/mokemokechicken/reversi-alpha-zero, https://github.com/Akababa/Chess-Zero, https://github.com/TDteach/AlphaZero_ChineseChess
3. A Chinese chess engine with gui: https://github.com/mm12432/MyChess


## Help to train

In order to build a strong chinese chess AI following the same type of techniques as AlphaZero, we need to do this with a distributed project, as it requires a huge amount of computations.

If you want to join us to build the best chinese chess AI in the world:

* For instructions, see [wiki](https://github.com/NeymarL/ChineseChess-AlphaZero/wiki)
* For live status, see https://cczero.org

![elo](elo.png)


## Environment

* Python 3.6.3
* tensorflow-gpu: 1.3.0
* Keras: 2.0.8


## Modules

### Reinforcement Learning

This AlphaZero implementation consists of two workers: `self` and  `opt`.

* `self` is Self-Play to generate training data by self-play using BestModel.
* `opt` is Trainer to train model, and generate new models.

For the sake of faster training, another two workers are involved:

* `sl` is Supervised learning to train data crawled from the Internet.
* `eval` is Evaluator to evaluate the NextGenerationModel with the current BestModel.

### GUI

Requirement: pygame

```bash
python cchess_alphazero/run.py play
```

**Screenshots**

![board](screenshots/board.png)

You can choose different board/piece styles and sides, see [play with human](#play-with-human).


## How to use

### Setup

### install libraries
```bash
pip install -r requirements.txt
```

If you want to use CPU only, replace `tensorflow-gpu` with `tensorflow` in `requirements.txt`.

Make sure Keras is using Tensorflow and you have Python 3.6.3+.

### Configuration

**PlayDataConfig**

* `nb_game_in_file, max_file_num`: The max game number of training data is `nb_game_in_file * max_file_num`.

**PlayConfig, PlayWithHumanConfig**

* `simulation_num_per_move` : MCTS number per move.
* `c_puct`: balance parameter of value network and policy network in MCTS.
* `search_threads`: balance parameter of speed and accuracy in MCTS.
* `dirichlet_alpha`: random parameter in self-play.

### Basic Usage

#### Self-Play

```
python cchess_alphazero/run.py self
```

When executed, Self-Play will start using BestModel. If the BestModel does not exist, new random model will be created and become BestModel.

options

* `--new`: create new BestModel
* `--type mini`: use mini config, (see `cchess_alphazero/configs/mini.py`)
* `--gpu '1'`: specify which gpu to use
* `--ucci`: whether to play with ucci engine (rather than self play, see `cchess_alphazero/worker/play_with_ucci_engine.py`)
* `--distributed`: run self play in distributed mode which means it will upload the play data to the remote server and download latest model from it

#### Trainer

```
python cchess_alphazero/run.py opt
```

When executed, Training will start. The current BestModel will be loaded. Trained model will be saved every epoch as new BestModel.

options

* `--type mini`: use mini config, (see `cchess_alphazero/configs/mini.py`)
* `--total-step TOTAL_STEP`: specify total step(mini-batch) numbers. The total step affects learning rate of training.
* `--gpu '1'`: specify which gpu to use

**View training log in Tensorboard**

```
tensorboard --logdir logs/
```

And access `http://<The Machine IP>:6006/`.

#### Play with human

```
python cchess_alphazero/run.py play
```

When executed, the BestModel will be loaded to play against human.

options

* `--ai-move-first`: if set this option, AI will move first, otherwise human move first.
* `--type mini`: use mini config, (see `cchess_alphazero/configs/mini.py`)
* `--gpu '1'`: specify which gpu to use
* `--piece-style WOOD`: choose a piece style, default is `WOOD`
* `--bg-style CANVAS`: choose a board style, default is `CANVAS`
* `--cli`: if set this flag, play with AI in a cli environment rather than gui

**Note**: Before you start, you need to download/find a font file (`.ttc`) and rename it as `PingFang.ttc`, then put it into `cchess_alphazero/play_games`. I have removed the font file from this repo because it's too big, but you can download it from [here](http://alphazero.52coding.com.cn/PingFang.ttc).

#### Evaluator

```
python cchess_alphazero/run.py eval
```

When executed, evaluate the NextGenerationModel with the current BestModel. If the NextGenerationModel does not exist, worker will wait until it exists and check every 5 minutes.

options

* `--type mini`: use mini config, (see `cchess_alphazero/configs/mini.py`)
* `--gpu '1'`: specify which gpu to use

#### Supervised Learning

```
python cchess_alphazero/run.py sl
```

When executed, Training will start. The current SLBestModel will be loaded. Tranined model will be saved every epoch as new SLBestModel.

*About the data*

I have two data sources, one is downloaded from https://wx.jcloud.com/market/packet/10479 ; the other is crawled from http://game.onegreen.net/chess/Index.html (with option --onegreen).

options

* `--type mini`: use mini config, (see `cchess_alphazero/configs/mini.py`)
* `--gpu '1'`: specify which gpu to use
* `--onegreen`: if set the flag, `sl_onegreen` worker will start to train data crawled from `game.onegreen.net`
* `--skip SKIP`: if set this flag, games whoses index is less than `SKIP` would not be used to train (only valid when `onegreen` flag is set)