Hi!
On Fri, Dec 19, 2014 at 10:50:30AM +0900, Hiroshi Yamashita wrote:
One question: Is there a place where I can find sgf
Paper author, Christopher Clark kindly sent me sgf and let me share on ML.
That's great, thanks for negotiating that. :-)
This is a copy of sgf.
That's pretty good looking for a pure predictor. Considering it has no
specific knowledge about semeais, ladders, or ko threat situations...
Switching out the pattern matcher (not the whole move generator) in an
existing mc program, should be pretty straightforward. Even if the nn is a
lot slower
Hi,
The predictor is white. It really does just play shapes, but evidently
it's plenty enough sometimes or against weaker opponents.
I saw some games, and my impression are
DCNN sees board widely.
Without previous move info, DCNN can answer opponent move.
It knows well corner life and death
This is Aya's move predictor(W) vs GNU Go(B).
http://eidogo.com/#3BNw8ez0R
I think previous move effect is too strong.
This is a good example of why a good playout engine will not necessarily play
well. The purpose of the playout policy is to *balance* errors. Following your
opponent's last
Hi Aja
We've just submitted our paper to ICLR. We made the draft available at
http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf
I hope you enjoy our work. Comments and questions are welcome.
I did not look at the go content, on which I'm no expert.
But for the network training, you might be
On Sat, Dec 20, 2014 at 12:17 AM, Aja Huang ajahu...@google.com wrote:
We've just submitted our paper to ICLR. We made the draft available at
http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf
Hi Aja,
Wow, very impressive. In fact so impressive, it seems a bit
suspicious(*)... If this is real
I put two commented games on
http://webdocs.cs.ualberta.ca/~mmueller/fuego/Convolutional-Neural-Network.html
http://webdocs.cs.ualberta.ca/~mmueller/fuego/Convolutional-Neural-Network.html
Enjoy!
Martin
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Hi Aja,
We've just submitted our paper to ICLR. We made the draft available at
http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf
97.2% against GNU Go?! Accuracy is 55%?! Incredible!
Thanks for the paper!
But it looks playing strength is similar to Clark's CNN.
MCTS with CNN is interesting.
Hi Martin,
I put two commented games on
http://webdocs.cs.ualberta.ca/~mmueller/fuego/Convolutional-Neural-Network.html
Thank you for the report. It was fun.
I'm also surprised CNN can play move 185 in Game 1.
CNN uses 1, 2, or 3 or more liberties info. B libs changed
from 4 to 3. And W libs
On Sun Dec 14 23:53:45 UTC 201, Hiroshi Yamashita wrote:
Teaching Deep Convolutional Neural Networks to Play Go
http://arxiv.org/pdf/1412.3409v1.pdf
Wow, this resembles somewhat what I was hoping to do! But now I
should look for some other avenue :-) But
I'm surprised it's only published on
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