When I had an opportunity to talk to Yann LeCun about a month ago, I asked him if anybody had used convolutional neural networks to play go and he wasn't aware of any efforts in that direction. This is precisely what I had in mind. Thank you very much for the link, Hiroshi!
I have been learning about neural networks recently, I have some basic implementation (e.g., no convolutional layers yet), and I am working hard on getting GPU acceleration. One of the things I want to do with NNs is almost exactly what's described in this paper (but including number of liberties and chain length as inputs). I also want to try to use a similar NN to initialize win/loss counts in new nodes in the tree in a MCTS program. One last thing I want to experiment with is providing the results of MC simulations as inputs to the network to improve its performance. I think there is potential for MC to help NNs and viceversa. Does anyone else in this list have an interest in applying these techniques to computer go? Álvaro. On Sun, Dec 14, 2014 at 6:53 PM, Hiroshi Yamashita <y...@bd.mbn.or.jp> wrote: > > Hi, > > This paper looks very cool. > > Teaching Deep Convolutional Neural Networks to Play Go > http://arxiv.org/pdf/1412.3409v1.pdf > > Thier move prediction got 91% winrate against GNU Go and 14% > against Fuego in 19x19. > > Regards, > Hiroshi Yamashita > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go
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