AlphaGo-develop源码

# 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](http://deepmind.com/alpha-go.html). This implementation uses Python and Keras - a decision to prioritize code clarity, at least in the early stages. [](https://travis-ci.org/Rochester-NRT/AlphaGo) # 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) # How to contribute See the ['Contributing'](CONTRIBUTING.md) document.