Hi All,
I wanted to share an update to a post I wrote last year about using the AlphaGo
Zero algorithm on small boards (7x7). I train for approximately 2 months on a
single desktop PC with 2 GPU cards.
In the article I was getting mediocre performance from the networks. Now, I've
found that there was a bug in the way that I was evaluating the networks and
that what I've been training seems to be matching GNU Go's level of performance.
Anyway, I'm aware I'm not exactly pushing the bounds of what's been done
before, but I thought some might be interested to see how one can still get
decent performance, at least in my opinion, on extremely limited hardware
setups -- orders of magnitude less than what DeepMind (and Leela) have used.
The post where I talk about the model's performance, training, and setup:
https://medium.com/@cody2007.2/how-i-trained-a-self-supervised-neural-network-to-beat-gnugo-on-small-7x7-boards-6b5b418895b7
A video where I play the network and show some of its move probabilities during
self-play games:
https://www.youtube.com/watch?v=a5vq1OjZrCU
The model weights and tensorflow code:
https://github.com/cody2007/alpha_go_zero_implementation
-Cody
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