I've been working on my own AlphaGo replication (code on github https://github.com/brilee/MuGo), and I've found it reasonably easy to hit 45% prediction rate with basic features (stone locations, liberty counts, and turns since last move), and a relatively small network (6 intermediate layers, 32 filters in each layer), using Adam / 10e-4 learning rate. This took ~2 hours on a GTX 960.
As others have mentioned, learning shoots up sharply at the start, and there is an extremely slow but steady improvement over time. So I'll experiment with fleshing out more features, increasing size of network, and longer training periods. Brian
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