EJGo

by Edan Meyer

What it is

This is a bot I am developing to play the game Go. The bot uses a deep convolutional network and trains of the moves of skilled (6 dan+) players to choose its moves. It currently has an accuracy of about 52% in choosing the correct moves. In comparison, the state of the art Alpha Go bot holds an accuracy of about 57%.

Install

Prereqs

• Tensorflow
• Tflearn

Setup

1. To install EJGo, first clone the repository to your computer.
2. Download sgfmill and put the sgfmill folder in the main directory of the repository
3. Download sgf files for training (preferably at least 20,000+ files). Create a new folder called kifu in the main directory and place the sgf files in the kifu folder.
4. Download and install GNUGo, and make sure that the command gnugo --mode gtp --level 10 --chineese-rules --positional-superko works without error. On Windows this can be achieved through putting the executable and attached dlls in the repository main directory. On Ubuntu GNUGo can be installed with the terminal command sudo apt-get install gnugo.
5. (Optional) Download the kgsgtp.jar file to be able to run the bot on the KGS Server. Add an ioproperties.ini file as per the specifications that come with the kgsgtp download.

Models

All current network models are deep convolutional networks and can be found in the "models" folder. Each file is a seperate network model. The current best accuracy model is the ejmodel at about 50% accuracy. The network was based off the best network used here. The network consists of 12 convolutional layers with elu activations and one fully connected layer followed with a softmax activation at the end. The first filter is 5x5, and the rest are 3x3. The model takes a 19x19x20 input array, 19x19 for the board size and 20 feature channels.

Note: Many models are outdated due to the fact that the input format for training changed several times during development

Adding a New Model

To add a new model create a new python file in the models folder. The name of the file will be the name of the model itself. The name is restricted to letters only, no numbers or special characters. Every model file should contain a function called get_network() that contains and returns the network. I would recommend using ejmodel.py as a template.

Usage

Flags

• n: number of games to use
• m: name of model to use
• s: true to continue off a previous save or false to start with new weights(defaults to false)

Commands

• Training: python train_network.py -n num_games -m model_name -s save, eg: python train_network.py -n 50000 -m ejmodel -s false

• Accuracy Testing: python test_accuracy.py -n num_games -m model_name, eg: python test_accuracy.py -n 100 -m ejmodel

• Host Bot on KGS Server:

  • Windows: Run StartServer.bat
  • Linux: sh StartServer.sh

• Play Local Game Against Bot: python play_game.py -m model_name, eg: python play_game.py -m ejmodel

File Purposes

• go_nn.py handles the forward propogation and network training
• board_3d.py contains functions to manipulate the board and build feature channels
• board.py is an old version of the board functions for 2D boards without feautre channels
• global_vars_go.py holds global variables when running the progarm
• loader.py takes care of loading models to tensorflow from the models folder
• Engine.py and TFEngine.py take care of basic engine functions for running games
• GTP.py communicates between the engine and uses Go to Text Protocol to make the bot easily compatible with most servers
• KGSEngine.py handles the engine specifically for the KGS Go Server
• HelperEngine.py communicates between the engine and GNU Go to know when to pass (passing is not handled by my bot)
• board_ui.py creates a Tkinter UI to play against the bot locally

Compatibility

EJGo was originally developed on Windows 10, so it should be fully compatible with Windows. It should be also mostly compatible with Linux, but it is a work in progress, so there are likely to be more errors. Please submit any errors as an issue so I can fix them up.

Acknowledgment

TheDuck314's go-NN repository was tremendously helpful. I used their Engine.py, GTP.py, TFEngine.py, KGSEngine.py, and TFEngine.py to handle nearly everything server communication related.

Thank you!