Implementation of Go compatible with Jeff Bradberry's board game framework
This is a python implementation of the board game Go. It is compatible
with the same board game framework used by
ultimate_tictactoe.
The project attempts to correctly implement the basic rules: Area
scoring, positional superko, and termination of the game triggered by
2 consecutive passes.
The idea with this project is to enable some experimentation with
machine learning inspired by the AlphaGo and AlphaZero projects.
Tested with this version of python:
To set up your local environment you should create a virtualenv and
install everything into it.
$ virtualenv go
Pip install this repo, either from a local copy,
$ pip install -e go
or from github,
$ pip install git+https://github.com/rtrusso/go
and then install the requirements
$ pip install -r requirements_server.txt$ pip install -r requirements_player.txt
Please note these currently point to the forked copy of the
repositories updated for python3, instead of the original repo created
by jbradberry. This will be corrected once the python3 PRs are picked
up in the original repos.
To run the server with Go
$ board-serve.py go
Optionally, the server ip address and port number can be added
$ board-serve.py go 0.0.0.0$ board-serve.py go 0.0.0.0 8000
To connect a client as a human player
$ board-play.py go human$ board-play.py go human 192.168.1.1 8000 # with ip addr and port
or with the AI player, see jbradberry/mcts (link to python3 update fork)
$ board-play.py go jrb.mcts.uct