miracle-wcg / alphago

A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search," details of which can be found on their website.

AlphaGo Replication

This project is a replication/reference implementation of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search," details of which can be found on their website. This implementation uses Python and Keras - a decision to prioritize code clarity, at least in the early stages.

Current project status

This is not yet a full implementation of AlphaGo. Development is being carried out on the develop branch.

We are still early in development. There are quite a few pieces to AlphaGo that can be written in parallel. We are currently focusing our efforts on the supervised and "self-play" parts of the training pipeline because the training itself may take a very long time..

Updates were applied asynchronously on 50 GPUs... Training took around 3 weeks for 340 million training steps

-Silver et al. (page 8)

For now you can only run some tests, as described in the 'Contributing' document.

How to contribute

See the 'Contributing' document.