Congratulation to DeepMind! This is a reengineering implementation (on behalf of many other git repo in /support/) of DeepMind's Oct19th publication: [Mastering the Game of Go without Human Knowledge]. The supervised learning approach is more practical for individuals. (This repository has single purpose of education only)
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AlphaGOZero (python tensorflow implementation)

This is a trial implementation of DeepMind's Oct19th publication: Mastering the Game of Go without Human Knowledge.

DeepMind release AlphaZero Teaching Go. It's a lot of fun!

From Paper

Pure RL has outperformed supervised learning+RL agent

SL evaluation

Download trained model


  2. Place under ./savedmodels/large20/

Set up

Install requirement

python 3.6 tensorflow/tensorflow-gpu

pip install -r requirement.txt

Download Dataset (kgs 4dan)

Under repo's root dir

cd data/download
chmod +x

Preprocess Data

It is only an example, feel free to assign your local dataset directory

python preprocess ./data/SGFs/kgs-*

Train A Model

python --mode=train

Play Against An A.I.

python --mode=gtp —-gtp_poliy=greedypolicy --model_path='./savedmodels/your_model.ckpt'

Play in Sabaki

  1. In console:
which python

add result to the headline of with #! prefix.

  1. Add the path of to Sabaki's manage Engine with argument --mode=gtp


  • AlphaGo Zero Architecture
  • Supervised Training
  • Self Play pipeline
  • Go Text Protocol
  • Sabaki Engine enabled
  • Tabula rasa (failed)
  • Distributed learning

Credit (orderless):

*Brain Lee *Ritchie Ng *Samuel Graván *森下 健 *yuanfengpang