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A scaled down version of AlphaGo Zero, playing on a 5x5 board
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Examples
GoGame
MCTS
Model
Selfplay
Shared
Trained_Models
Training
.gitignore
README.md
__init__.py
alphago_zero.pdf
eval.py
play.py
pretrain.py
requirements.txt
score.py
test.py
train.py
video_img.png

README.md

BetaGo

A scaled down version of AlphaGo Zero, playing on a 5x5 board

Video Demo

BetaGo

Overview

This projects aims to reimplement the AlphaGo Zero paper to play on a 5x5 board.

AlphaGo Zero uses end-to-end reinforcement learning to learn Go from scratch, without any human player data.

Project Architecture

  • GoGame: Go simulation code.

  • MCTS: Implmentation for Monte-carlo tree search.

  • Model: Neural network architecture

  • Selfplay: Module for managing the games between agents

  • Shared: Miscellaneous functions and constants

  • Training: Class for generating gameplay data and performing training and evaluation

To Run

Generate games and train the model:

python3 train.py

Test a trained model:

python3 test.py
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