Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

On 3/17/15, David Silver wrote: > Reinforcement learning is different to unsupervised learning. We used > reinforcement learning to train the Atari games. Also we published a more > recent paper (www.nature.com/articles/nature14236) that applied the same > network to 50 different Atari games (achi

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Hi Oliver Reinforcement learning is different to unsupervised learning. We used reinforcement learning to train the Atari games. Also we published a more recent paper (www.nature.com/articles/nature14236) that applied the same network to 50 different Atari games (achieving human level in around ha

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Can you say anything about whether you think their approach to unsupervised learning could be applied to networks similar to those you trained? Any practical or theoretical constraints we should be aware of? On Monday, 16 March 2015, Aja Huang wrote: > Hello Oliver, > > 2015-03-16 11:58 GMT+00:0

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

On Wed, Dec 31, 2014 at 9:29 PM, Hugh Perkins wrote: > I started building a convolution network library for OpenCL at > https://github.com/hughperkins/ClConvolve/ > Apparently building a convolutional neural network library is non-trivial :-P Anyway, I got up to 99.55% on mnist, and started loo

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

@computer-go.org Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go But, I imagine this is more fuss than it is worth; the NN will be integrated into MCTS search, and I think the strong programs already have ways to generate ko threat candidates. Darren Do

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

> But, I imagine this is more fuss than it is worth; the NN will be > integrated into MCTS search, and I think the strong programs already > have ways to generate ko threat candidates. > > Darren > Do they? What would look like? Playing 2 moves in a row for the same side? I thought the programs na

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

On 2014-12-19 15:25, Hiroshi Yamashita wrote: > Ko fight is weak. Ko threat is simpley good pattern move. I suppose you could train on a subset of data: only positions where there was a ko-illegal move on the board. Then you could learn ko threats. And then use this alternative NN when meeting a k

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

> I would very much appreciate an open source implementation of this - or rather, I'd rather spend my time using one to do interesting things rather than building one, I do plan to open source my implementation if I have to make one and can bring myself to build one from scratch... I started build

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Am 31.12.2014 um 14:05 schrieb Petr Baudis: Hi! On Wed, Dec 31, 2014 at 11:16:57AM +0100, Detlef Schmicker wrote: I am just trying to reproduce the data from page 7 with all features disabled. I do not reach the accuracy (I stay below 20%). Now I wonder about a short statement in the paper

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Hi! On Wed, Dec 31, 2014 at 11:16:57AM +0100, Detlef Schmicker wrote: > I am just trying to reproduce the data from page 7 with all features > disabled. I do not reach the accuracy (I stay below 20%). > > Now I wonder about a short statement in the paper, I did not really > understand: > On pag

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Hi, I am just trying to reproduce the data from page 7 with all features disabled. I do not reach the accuracy (I stay below 20%). Now I wonder about a short statement in the paper, I did not really understand: On page 4 top right they state "In our experience using the rectifier function wa

[Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

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

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

I put two commented games on http://webdocs.cs.ualberta.ca/~mmueller/fuego/Convolutional-Neural-Network.html Enjoy! Martin ___ Computer-go mailing list Computer-

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

pponent's last play is very helpful in that regard. -Original Message- From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Hiroshi Yamashita Sent: Friday, December 19, 2014 10:25 AM To: computer-go@computer-go.org Subject: Re: [Computer-go] Teaching Deep C

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

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

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

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 t

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

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. > http://www.y

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Hi, 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. This is a copy of sgf. http://www.yss-aya.com/dcnn_games_20141218.tar.gz His notes is as follows. ---

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Hi Ingo, One question: Is there a place where I can find sgf I could not find. I also want to see sgf. Hiroshi Yamashita ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

hita" > An: computer-go@computer-go.org > Betreff: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go > > Hi, > > This paper looks very cool. > > Teaching Deep Convolutional Neural Networks to Play Go > http://arxiv.org/pdf/1412.3409v1.pdf > &

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

a Huang Sent: Tuesday, December 16, 2014 10:23 AM To: computer-go@computer-go.org Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go Hi Brian, I understand your points, but deep convolutional neural networks are very powerful in the sense that they can r

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Hi Aja, That being said, Hiroshi, are you sure there was no problem in your experiment? 6% winning rate against GnuGo on 19x19 seems too low for a predictor of 38.8% accuracy. And yes, in the paper we will show a game that I tried without resign, but result is similar. winrate games 19

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Behalf Of René van de Veerdonk Sent: Monday, December 15, 2014 11:47 PM To: computer-go Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go Correct me if I am wrong, but I believe that the CrazyStone approach of team-of-features can be cast in terms of a shal

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

t;, "influence" that would > have to be provided to a DT as inputs. No free lunch, but again, this is > about betting that one technology is (overall) more suitable than another. > > > > > > > > *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On >

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

omputer-go.org] On Behalf Of Stefan Kaitschick Sent: Monday, December 15, 2014 6:37 PM To: computer-go@computer-go.org Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go Finally, I am not a fan of NN in the MCTS architecture. The NN architecture imposes

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

RE: MC + NN feedback: One area I'm particularly interested in is using NN to apply knowledge from the tree during the playout. I expect that NNs will have difficulty learning strong tactical play, but a combination of a pre-trained network with re-training based on the MCTS results might be able to

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

2014-12-15 23:29 GMT+00:00 Petr Baudis : > > Huh, aren't you? > > I just played quick two games GnuGoBot39 where I tried very hard not > to read anything at all, and had no trouble winning. (Well, one of my > groups had some trouble but mindless clicking saved it anyway.) That well explains

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

> > Finally, I am not a fan of NN in the MCTS architecture. The NN > architecture imposes a high CPU burden (e.g., compared to decision trees), > and this study didn't produce such a breakthrough in accuracy that I would > give away performance. > > > Is it really such a burden? Supporting the mov

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

On Mon, Dec 15, 2014 at 11:03:35PM +, Aja Huang wrote: > 2014-12-15 21:31 GMT+00:00 Petr Baudis : > > > > Still, strong play makes sense for a strong predictor. I believe I > > can also beat GNUGo >90% of time in blitz settings without doing pretty > > much *any* concious sequence reading.

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

2014-12-15 21:31 GMT+00:00 Petr Baudis : > > Still, strong play makes sense for a strong predictor. I believe I > can also beat GNUGo >90% of time in blitz settings without doing pretty > much *any* concious sequence reading. So I would expect a module that's > supposed to mirror my intuition t

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

On Mon, Dec 15, 2014 at 4:39 PM, Dave Dyer wrote: > > Combining simple metrics such as 3x3 neighborhood, position on the board, > and proximity to previous play, you can easily get to an average score > of 85%, without producing noticeably good play, at least without a search > to back it up. >

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

On 12/15/2014 01:39 PM, Dave Dyer wrote: You don't need a neural net to predict pro moves at this level. My measurement metric was slightly different, I counted how far down the list of moves the "pro" move appeared, so matching the pro move scored as 100% and being tenth on a list of 100 moves

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

You don't need a neural net to predict pro moves at this level. My measurement metric was slightly different, I counted how far down the list of moves the "pro" move appeared, so matching the pro move scored as 100% and being tenth on a list of 100 moves scored 90%. Combining simple metrics suc

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

On Mon, Dec 15, 2014 at 02:57:32PM -0500, Brian Sheppard wrote: > I found the 14% win rate against Fuego is potentially impressive, but I > didn't get a sense for Fuego's effort level in those games. E.g., Elo > ratings. MCTS actually doesn't play particularly well until a sufficient > investmen

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

such a breakthrough in accuracy that I would give away performance. -Original Message- From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Hiroshi Yamashita Sent: Monday, December 15, 2014 10:27 AM To: computer-go@computer-go.org Subject: Re: [Computer-go] Teaching

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

I tested Aya's move prediction strength. Prediction rate is 38.8% (first choice is same as pro's move) against GNU Go 3.7.10 Level 10 winrate games 19x19 0.059 607 13x13 0.170 545 9x9 0.1411020 I was bit surprised there is no big difference from 9x9 to 19x19. But 6%

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Chris Maddison also produced very good (in fact much better) results using a deep convolutional network during his internship at Google. Currently waiting for publication approval, I will post the paper once it is passed. Aja On Mon, Dec 15, 2014 at 2:59 PM, Erik van der Werf wrote: > > Thanks f

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Thanks for posting this Hiroshi! Nice to see this neural network revival. It is mostly old ideas, and it is not really surprising to me, but with modern compute power everyone can now see that it works really well. BTW for some related work (not cited), people might be interested to read up on the

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Hi! On Mon, Dec 15, 2014 at 08:53:45AM +0900, Hiroshi Yamashita wrote: > 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. T

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Álvaro, this is exactly something that I have been thinking about as well (the last part about MC+NN and feedback between the two). It seems like the authors of that paper are also thinking about something similar. I currently have the very basics of an implementation as well but performance is b

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

> 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. There was work using neural networks in the mid 1990s, when I first started with computer go. I t

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

A move generator, that always plays it's first choice, that can win games against Fuego? That smells like a possible game changer.(pardon the pun). Surely, programmers will take this workhorse, and put it before the MC cart. Stefan ___ Computer-go mailin

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

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 ab

[Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

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-