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)
Switch branches/tags
Nothing to show
Clone or download

README.md

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

  1. https://drive.google.com/drive/folders/1Xs8Ly3wjMmXjH2agrz25Zv2e5-yqQKaP?usp=sharing

  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 download.sh
./download.sh

Preprocess Data

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

python preprocess.py preprocess ./data/SGFs/kgs-*

Train A Model

python main.py --mode=train

Play Against An A.I.

python main.py --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 main.py with #! prefix.

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

TODO:

  • 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