This repository holds code for the A.I. model I made for playing the board game of Go. I am to develop the algorithms used to develop AlphaGo Zero, in a lite version. It also holds web application code for a Go-playing website where users can play against each other or play against the A.I.
It is still a work in progress.
cd play_go_web_app
On one terminal, perform:
python manage.py runserver
On a separate terminal, perform:
cd frontend
npm run dev
| File | Description |
|---|---|
| Frontend | Frontend displaying multiplayer, real-time gameplay, with abilities for users to create their own rooms or join a room. |
| Backend | Backend with an API responsible for fetching information about rooms from a database. |
| test_tf.py | the TensorFlow quickstart tutorial code (used for testing purposes on a Linux-based computer science machine found at my college) |
| mcts.py | holds the MonteCarloTree class that is responsible for the underlying data structures and algorithms to ultimately run the big algorithm in the room: Monte Carlo Tree Search |
| politer-selfplay.py | responsible for the policy iteration algorithm and executing each episode, all for the purpose of training and updating the neural network involved |
- TechWithTim's Music Controller Web App Tutorial helped us immensely with starting this project.
- Django channels tutorial from the docs helped us build sockets for real-time communication across clients viewing or partiicpating in gameplay inside a Room.