
Teaching Deep Convolutional Neural Networks to Play Go [pdf] - lainon
https://pdfs.semanticscholar.org/7e03/1a05fb2b26ede4fe9ab21d7a9c0fa20bd4e8.pdf
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amelius
> To solve this problem we introduce a number of novel techniques, including a
> method of tying weights in the network to ‘hard code’ symmetries that are
> expect to exist in the target function [...]

Shouldn't the neural network be able to figure out those symmetries by itself?
And if not, why not?

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lainon
Wups, I've just noticed myself that this is a repost:
[https://news.ycombinator.com/item?id=8753347](https://news.ycombinator.com/item?id=8753347)

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zump
The first comment of that thread predicted AlphaGo! :O

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dontreact
Biggest fundamental difference: no policy network learned via reinforcement
learning. AlphaGo = MCTS + Value network + (Policy network). I think that that
piece is pretty important and is what allowed AlphaGo to improve so much with
self-play.

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rocqua
Aparantly, just the policy network of alpha go plays at a decent level (around
1kyu - 1Dan IIRC).

Alphago started with the policy network and later build the rest around that.

