Move Evaluation in Go Using Deep Convolutional Neural Networks · HackerPapers discussions

Move Evaluation in Go Using Deep Convolutional Neural Networks — mentioned by Jach

Fri, 01 Jan 2016 06:48:59 GMT

I read http://arxiv.org/abs/1412.6564 earlier this year and tldr they trained a deep net bot that managed to hold about 1-2d on KGS. Since there's no searching going on, that's pretty good, though not as good as CrazyStone or other MCTS bots. (I hadn't heard they went from 6d to 5d, but sure enough http://www.gokgs.com/graphPage.jsp?user=CrazyStone I wonder what happened?) I suspect combining the two methods will yield even stronger programs, I've heard some folks at Facebook and Google are each working on their own Go bots based on deep learning. If some company spent a few years building…

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Move Evaluation in Go Using Deep Convolutional Neural Networks — mentioned by deepnet

Tue, 03 Nov 2015 19:41:07 GMT

This Facebook Video suggests it has had months of either training or development, which sounds might be it learning by playing, reinforcement learning in this space could be very interesting - more details would be good. The two breakthrough papers using Deep Conv Nets to play Go - trained on Expert Games to predict the Experts Next Move. This is made possible by the huge archives of expert play from online Go servers that have reached Big Data sizes in the last few years. Teaching Deep Convolutional Neural Networks to Play Go - Clark & Storkey Edinburgh Informatics 2015…

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Move Evaluation in Go Using Deep Convolutional Neural Networks — mentioned by deepnet

Thu, 30 Jul 2015 03:16:28 GMT

and the follow up from Google's Deepmind group : Move Evaluation in Go Using Deep Convolutional Neural Networks Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver http://arxiv.org/abs/1412.6564

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Move Evaluation in Go Using Deep Convolutional Neural Networks — mentioned by deepnet

Wed, 29 Jul 2015 20:49:13 GMT

Monte Carlo Tree Search ( Random playout ) is currently the best computer strategy for evaluating a Go position. This is likely due to the way Go works , random playout provides a rough estimate of who controls what territory ( this is how Go is scored ). Recently two deep-learning papers showed very impressive results. http://arxiv.org/abs/1412.3409 http://arxiv.org/abs/1412.6564 The neural networks were tasked with predicting what move an expert would make given a position. The MCTS takes a long time 100,000 playouts are typical - once trained the neural nets are orders of magnitude faster.…

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Move Evaluation in Go Using Deep Convolutional Neural Networks — mentioned by karmakaze

Wed, 27 May 2015 15:38:57 GMT

What I find shocking isn't only the level of play by machines, but also the number of techniques that can be applied to achieve it. Traditionally they were very domain knowledge based and now they tend to be hybrids. This for me really quantified how deep into the age of AI we're in. I never expected Go to be approched this quickly as the search space is so vast. AyaMC (AyaBot 3d on KGS) was Aya[1] rewritten as a Monte Carlo simulation. Many Faces of Go[2] (ManyFaces 2d on KGS) has been the strongest sold Go program which has transitioned to using Monte Carlo Tree Search. NiceGo19N, was…

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Move Evaluation in Go Using Deep Convolutional Neural Networks — mentioned by deepnet

Wed, 13 May 2015 09:12:20 GMT

*references Teaching Deep Convolutional Neural Networks to Play Go by Clarke & Storkey http://arxiv.org/abs/1412.3409 Move Evaluation in Go Using Deep Convolutional Neural Networks by Maddison , Huang , Silver & Sutskever http://arxiv.org/abs/1412.6564 Tesauro's TD-Gammon http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node108.htm... Playing Atari with Deep Reinforcement Learning , Mnih et al. https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf Otoro's CNE Bibliography http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/ Neuro Evolution - http://en.wikipedia.org/wiki/Neuroevolution

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Move Evaluation in Go Using Deep Convolutional Neural Networks — HN submission

Wed, 18 Feb 2015 18:04:24 GMT

Move Evaluation in Go Using Deep Convolutional Neural Networks

10 points; 1 discussion reply on HN. Paper and source history. A submission is not an endorsement.