(Previously known just as "AlphaGo," renamed to clarify that we are not affiliated with DeepMind)
This project is a student-led 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.
This is not yet a full implementation of AlphaGo. Development is being carried out on the develop
branch. The current emphasis is on speed optimizations, which are
necessary to complete training of the value-network and to create
feasible tree-search. See the cython-optimization branch for more.
Selected data (i.e. trained models) are released in our data repository.
This project has primarily focused on the neural network training
aspect of DeepMind's AlphaGo. We also have a simple single-threaded
implementation of their tree search algorithm, though it is not fast
enough to be competitive yet.
See the wiki page on the training pipeline for information on how to run the training commands.