Re: [Computer-go] Accelerating Self-Play Learning in Go

On 8/03/19 16:14, David Wu wrote: > I suspect Leela Zero would come off as far *less* favorable if one > tried to do such a comparison using their actual existing code rather > than abstracting down to counting neural net evals, because as far as > I know in Leela Zero there is no cross-game

Re: [Computer-go] A new ELF OpenGo bot and analysis of historical Go games

On 17/02/19 23:24, Hiroshi Yamashita wrote: > Hi Ingo, > >> * How strong is the new ELF bot in comparison with Leela-Zero? > > from CGOS BayesElo, new ELF(ELFv2) is about +100 stronger than Leela-Zero. We ran a test match and ELFv2 lost 34 - 62 against LZ-204 at 1600 visits each, so that's

Re: [Computer-go] GCP passing on the staff ...

On 29/01/19 11:23, Petri Pitkanen wrote: > Just purely curiosity: How strong is Leela now? googling up gives that > it is better than best humasn already? Is that true? The network is over 100 Elo stronger than the second generation of ELF, which was about 100 Elo stronger than the first

Re: [Computer-go] AI Ryusei 2018 result

On 17/12/18 01:53, Hiroshi Yamashita wrote: > Hi, > > AI Ryusei 2018 was held on 15,16th December in Nihon-kiin, Japan. > 14 programs played preliminary swiss 7 round, and top 6 programs >  played round-robin final. Then, Golaxy won. > > Result >

Re: [Computer-go] Message by Facebook AI group

On 5/05/2018 7:30, "Ingo Althöfer" wrote: > It was meant from the viewpoint of an > outside observer/commentator. > > In Germany we have a proverb: > "Konkurrenz belebt das Geschaeft." > Roughly translated: > "Competition enlivens the bbusiness." So does cooperation. Thanks to Facebook for

Re: [Computer-go] Message by Facebook AI group

On 3/05/2018 5:24, "Ingo Althöfer" wrote: > Hello, > > in the German computer go forum a link to this message by the > Facebook AI Research group was posted: > https://research.fb.com/facebook-open-sources-elf-opengo/ FYI, we were able to convert the Facebook network into Leela Zero format,

[Computer-go] Leela Zero on 9x9

There has been some discussion whether value networks can "work" on 9x9 and whether the bots can beat the best humans. While I don't expect this to resolve the discussion, Leela Zero now tops the CGOS 9x9 list. This seems to be entirely the work of a single user who has ran 3.2M self-play games

Re: [Computer-go] PUCT formula

On 09-03-18 18:03, Brian Sheppard via Computer-go wrote: > I am guessing that Chenjun and Martin decided (or knew) that the AGZ > paper was incorrect and modified the equation accordingly. > I doubt it's just the paper that was incorrect, given that the formula has been given without log

Re: [Computer-go] PUCT formula

On 08-03-18 18:47, Brian Sheppard via Computer-go wrote: > I recall that someone investigated this question, but I don’t recall the > result. What is the formula that AGZ actually uses? The one mentioned in their paper, I assume. I investigated both that and the original from the referenced

Re: [Computer-go] Crazy Stone is back

On 5/03/2018 12:28, valky...@phmp.se wrote: > Remi twittered more details here (see the discussion with gghideki: > > https://twitter.com/Remi_Coulom/status/969936332205318144 Thank you. So Remi gave up on rollouts as well. Interesting "difference of opinion" there with Zen. Last time I tested

Re: [Computer-go] 9x9 is last frontier?

On 5/03/2018 10:54, Dan wrote: > I believe this is a problem of the MCTS used and not due > to for lack of training.  > > Go is a strategic game so that is different from chess that is full of > traps.      Does the Alpha Zero result not indicate the opposite, i.e. that MCTS is workable? --

Re: [Computer-go] Crazy Stone is back

On 28-02-18 07:13, Rémi Coulom wrote: > Hi, > > I have just connected the newest version of Crazy Stone to CGOS. It > is based on the AlphaZero approach. In that regard, are you still using Monte Carlo playouts? -- GCP ___ Computer-go mailing list

Re: [Computer-go] 9x9 is last frontier?

On 02-03-18 17:07, Dan wrote: > Leela-chess is not performing well enough I don't understand how one can say that given that they started with the random network last week only and a few clients. Of course it's bad! That doesn't say anything about the approach. Leela Zero has gotten strong but

Re: [Computer-go] MCTS with win-draw-loss scores

On 13-02-18 16:05, "Ingo Althöfer" wrote: > Hello, > > what is known about proper MCTS procedures for games > which do not only have wins and losses, but also draws > (like chess, Shogi or Go with integral komi)? > > Should neural nets provide (win, draw, loss)-probabilities > for positions in

Re: [Computer-go] MiniGo open sourced

On 30-01-18 20:59, Álvaro Begué wrote: > Chrilly Donninger's quote was probably mostly true in the 90s, but > it's now obsolete. That intellectual protectionism was motivated by > the potential economic profit of having a strong engine. It probably > slowed down computer chess for decades, until

Re: [Computer-go] Nvidia Titan V!

On 08-12-17 09:29, Rémi Coulom wrote: > Hi, > > Nvidia just announce the release of their new GPU for deep learning: > https://www.theverge.com/2017/12/8/16750326/nvidia-titan-v-announced-specs-price-release-date > > "The Titan V is available today and is limited to two per > customer." > >

Re: [Computer-go] action-value Q for unexpanded nodes

On 03-12-17 21:39, Brian Lee wrote: > It should default to the Q of the parent node. Otherwise, let's say that > the root node is a losing position. Upon choosing a followup move, the Q > will be updated to a very negative value, and that node won't get > explored again - at least until all 362

Re: [Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

On 06-12-17 22:29, Brian Sheppard via Computer-go wrote: > The chess result is 64-36: a 100 rating point edge! I think the > Stockfish open source project improved Stockfish by ~20 rating points in > the last year. It's about 40-45 Elo FWIW. > AZ would dominate the current TCEC. I don't think

Re: [Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

On 06-12-17 21:19, Petr Baudis wrote: > Yes, that also struck me. I think it's good news for the community > to see it reported that this works, as it makes the training process > much more straightforward. They also use just 800 simulations, > another good news. (Both were one of the first

Re: [Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

On 6/12/2017 19:48, Xavier Combelle wrote: > Another result is that chess is really drawish, at the opposite of shogi We sort-of knew that, but OTOH isn't that also because the resulting engine strength was close to Stockfish, unlike in other games? -- GCP

Re: [Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

On 6/12/2017 18:57, Darren Cook wrote: >> Mastering Chess and Shogi by Self-Play with a General Reinforcement >> Learning Algorithm >> https://arxiv.org/pdf/1712.01815.pdf > > One of the changes they made (bottom of p.3) was to continuously update > the neural net, rather than require a new

Re: [Computer-go] action-value Q for unexpanded nodes

On 06-12-17 11:47, Aja Huang wrote: > All I can say is that first-play-urgency is not a significant > technical detail, and what's why we didn't specify it in the paper. I will have to disagree here. Of course, it's always possible I'm misunderstanding something, or I have a program bug that I'm

Re: [Computer-go] Is MCTS needed?

On 16-11-17 18:24, Stephan K wrote: > 2017-11-16 17:37 UTC+01:00, Gian-Carlo Pascutto <g...@sjeng.org>: >> Third, evaluating with a different rotation effectively forms an >> ensemble that improves the estimate. > > Could you expand on that? I understand rotatin

Re: [Computer-go] Is MCTS needed?

On 17-11-17 02:15, Hideki Kato wrote: > Stephan K: >

Re: [Computer-go] Is MCTS needed?

On 16-11-17 18:15, "Ingo Althöfer" wrote: > Something like MCTS would not work in chess, because in > contrast to Go (and Hex and Amazons and ...) Chess is > not a "game with forward direction". Ingo, I think the reason Petr brought the whole thing up is that AlphaGo Zero uses "MCTS" but it does

Re: [Computer-go] Is MCTS needed?

On 16/11/2017 16:43, Petr Baudis wrote: > But now, we expand the nodes literally all the time, breaking the > stationarity possibly in drastic ways. There are no reevaluations > that would improve your estimate. First of all, you don't expect the network evaluations to drastically vary between

Re: [Computer-go] what is reachable with normal HW

On 15-11-17 10:51, Petri Pitkanen wrote: > I think the intereseting question left now is: How strong GO-program one > can have in normal Laptop? TPU and GPU are fine for showing what can be > done but as practical tool for a go player the bot  has to run something > people can afford. And can buy

Re: [Computer-go] Nochi: Slightly successful AlphaGo Zero replication

On 11-11-17 00:58, Petr Baudis wrote: >>> * The neural network is updated after _every_ game, _twice_, on _all_ >>> positions plus 64 randomly sampled positions from the entire history, >>> this all done four times - on original position and the three >>> symmetry flips (but I was

Re: [Computer-go] Nochi: Slightly successful AlphaGo Zero replication

On 10/11/2017 1:47, Petr Baudis wrote: > * AlphaGo used 19 resnet layers for 19x19, so I used 7 layers for 7x7. How many filters per layer? FWIW 7 layer resnet (14 + 2 layers) is still pretty huge - larger than the initial AlphaGo. Given the amount of games you have, and the size of the

Re: [Computer-go] AlphaGo Zero Loss

On 7/11/2017 19:08, Petr Baudis wrote: > Hi! > > Does anyone knows why the AlphaGo team uses MSE on [-1,1] as the > value output loss rather than binary crossentropy on [0,1]? I'd say > the latter is way more usual when training networks as typically > binary crossentropy yields better result,

Re: [Computer-go] AlphaGo Zero self-play temperature

On 7/11/2017 19:07, Imran Hendley wrote: > Am I understanding this correctly? Yes. It's possible they had in-betweens or experimented with variations at some point, then settled on the simplest case. You can vary the randomness if you define it as a softmax with varying temperature, that's

Re: [Computer-go] Zero is weaker than Master!?

On 27-10-17 10:15, Xavier Combelle wrote: > Maybe I'm wrong but both curves for alphago zero looks pretty similar > except than the figure 3 is the zoom in of figure 6 The blue curve in figure 3 is flat at around 60 hours (2.5 days). In figure 6, at 2.5 days the line is near vertical. So it is

Re: [Computer-go] November KGS bot tournament

On 26-10-17 09:43, Nick Wedd wrote: > Please register by emailing me at mapr...@gmail.com > , with the words "KGS Tournament Registration" > in the email title. > With the falling interest in these events since the advent of AlphaGo, > it is likely that this will be the

Re: [Computer-go] Source code (Was: Reducing network size? (Was: AlphaGo Zero))

On 27-10-17 00:33, Shawn Ligocki wrote: > But the data should be different for different komi values, right? > Iteratively producing self-play games and training with the goal of > optimizing for komi 7 should converge to a different optimal player > than optimizing for komi 5. For the policy

Re: [Computer-go] Zero is weaker than Master!?

> For searching mention of the 20 blocks I search for 20 in the whole > paper and did not found any other mention > > than of the kifu thing. > > > Le 26/10/2017 à 15:10, Gian-Carlo Pascutto a écrit : > > On 26-10-17 10:55, Xavier Combelle wrote: > >> It

Re: [Computer-go] AlphaGo Zero

On 25-10-17 16:00, Petr Baudis wrote: > That makes sense. I still hope that with a much more aggressive > training schedule we could train a reasonable Go player, perhaps at > the expense of worse scaling at very high elos... (At least I feel > optimistic after discovering a stupid bug in my

Re: [Computer-go] Zero is weaker than Master!?

On 26-10-17 10:55, Xavier Combelle wrote: > It is just wild guesses based on reasonable arguments but without > evidence. David Silver said they used 40 layers for AlphaGo Master. That's more evidence than there is for the opposite argument that you are trying to make. The paper certainly

Re: [Computer-go] AlphaGo Zero

On 25-10-17 16:00, Petr Baudis wrote: >> The original paper has the value they used. But this likely needs tuning. I >> would tune with a supervised network to get started, but you need games for >> that. Does it even matter much early on? The network is random :) > > The network actually

Re: [Computer-go] Source code (Was: Reducing network size? (Was: AlphaGo Zero))

On 25-10-17 05:43, Andy wrote: > Gian-Carlo, I didn't realize at first that you were planning to create a > crowd-sourced project. I hope this project can get off the ground and > running! > > I'll look into installing this but I always find it hard to get all the > tool chain stuff going. I

[Computer-go] Source code (Was: Reducing network size? (Was: AlphaGo Zero))

On 23-10-17 10:39, Darren Cook wrote: >> The source of AlphaGo Zero is really of zero interest (pun intended). > > The source code is the first-hand account of how it works, whereas an > academic paper is a second-hand account. So, definitely not zero use. This should be fairly accurate:

Re: [Computer-go] AlphaGo Zero

On 21/10/2017 14:21, David Ongaro wrote: > I understand that DeepMind might be unable to release the source code > of AlphaGo due to policy or licensing reasons, but it would be great > (and probably much more valuable) if they could release the fully > trained network. The source of AlphaGo Zero

Re: [Computer-go] Zero performance

On 20/10/2017 22:48, fotl...@smart-games.com wrote: > The paper describes 20 and 40 block networks, but the section on > comparison says AlphaGo Zero uses 20 blocks. I think your protobuf > describes a 40 block network. That's a factor of two  They compared with both, the final 5180 Elo number

Re: [Computer-go] Zero performance

On 20/10/2017 22:41, Sorin Gherman wrote: > Training of AlphaGo Zero has been done on thousands of TPUs, > according to this source: > https://www.reddit.com/r/baduk/comments/777ym4/alphago_zero_learning_from_scratch_deepmind/dokj1uz/?context=3 > > Maybe that should explain the difference in

Re: [Computer-go] Zero performance

o do well. > > Álvaro. > > > On Fri, Oct 20, 2017 at 1:44 PM, Gian-Carlo Pascutto <g...@sjeng.org> > wrote: > >> I reconstructed the full AlphaGo Zero network in Caffe: >> https://sjeng.org/dl/zero.prototxt >> >> I did some performance measureme

Re: [Computer-go] AlphaGo Zero

On Fri, Oct 20, 2017, 21:48 Petr Baudis wrote: > Few open questions I currently have, comments welcome: > > - there is no input representing the number of captures; is this > information somehow implicit or can the learned winrate predictor > never truly approximate the

[Computer-go] Zero performance

I reconstructed the full AlphaGo Zero network in Caffe: https://sjeng.org/dl/zero.prototxt I did some performance measurements, with what should be state-of-the-art on consumer hardware: GTX 1080 Ti NVIDIA-Caffe + CUDA 9 + cuDNN 7 batch size = 8 Memory use is about ~2G. (It's much more for

Re: [Computer-go] AlphaGo Zero

On 19-10-17 13:00, Aja Huang via Computer-go wrote: > Hi Hiroshi, > > I think these are good questions. You can ask them at  > https://www.reddit.com/r/MachineLearning/comments/76xjb5/ama_we_are_david_silver_and_julian_schrittwieser/ It seems the question was indeed asked but not answered:

Re: [Computer-go] AlphaGo Zero

On 19-10-17 13:23, Álvaro Begué wrote: > Summing it all up, I get 22,837,864 parameters for the 20-block network > and 46,461,544 parameters for the 40-block network. > > Does this seem correct? My Caffe model file is 185887898 bytes / 32-bit floats = 46 471 974 So yes, that seems pretty close.

Re: [Computer-go] AlphaGo Zero

On 18-10-17 19:50, cazen...@ai.univ-paris8.fr wrote: > > https://deepmind.com/blog/ > > http://www.nature.com/nature/index.html Another interesting tidbit: The inputs don't contain a reliable board edge. The "white to move" plane contains it, but only when white is to move. So until AG Zero

Re: [Computer-go] AlphaGo Zero

On 18/10/2017 22:00, Brian Sheppard via Computer-go wrote: > This paper is required reading. When I read this team’s papers, I think > to myself “Wow, this is brilliant! And I think I see the next step.” > When I read their next paper, they show me the next *three* steps. Hmm, interesting way of

Re: [Computer-go] AlphaGo Zero

On 18/10/2017 22:00, Brian Sheppard via Computer-go wrote: > A stunning result. The NN uses a standard vision architecture (no Go > adaptation beyond what is necessary to represent the game state). The paper says that Master (4858 rating) uses Go specific features, initialized by SL, and the same

Re: [Computer-go] AlphaGo Zero

On 18/10/2017 19:50, cazen...@ai.univ-paris8.fr wrote: > > https://deepmind.com/blog/ > > http://www.nature.com/nature/index.html Select quotes that I find interesting from a brief skim: 1) Using a residual network was more accurate, achieved lower error, and improved performance in AlphaGo by

Re: [Computer-go] Deep Blue the end, AlphaGo the beginning?

On 18/08/2017 23:07, uurtamo . wrote: > They run on laptops. A program that could crush a grandmaster will run > on my laptop. That's an assertion I can't prove, but I'm asking you to > verify it or suggest otherwise. Sure. > Now the situation with go is different. For what it's worth, I would

Re: [Computer-go] Deep Blue the end, AlphaGo the beginning?

On 18/08/2017 20:34, Petr Baudis wrote: > You may be completely right! And yes, I was thinking about Deep Blue > in isolation, not that aware about general computer chess history. Do > you have some suggested reading regarding Deep Blue and its lineage and > their contributions to the field of

Re: [Computer-go] Deep Blue the end, AlphaGo the beginning?

On 17-08-17 21:35, Darren Cook wrote: > "I'm sure some things were learned about parallel processing... but the > real science was known by the 1997 rematch... but AlphaGo is an entirely > different thing. Deep Blue's chess algorithms were good for playing > chess very well. The machine-learning

Re: [Computer-go] Possible idea - decay old simulations?

On 24-07-17 16:07, David Wu wrote: > Hmm. Why would discounting make things worse? Do you mean that you > want the top move to drop off slower (i.e. for the bot to take longer > to achieve the correct valuation of the top move) to give it "time" > to search the other moves enough to find that

Re: [Computer-go] KGS Bot tournament July

On 9/07/2017 17:41, "Ingo Althöfer" wrote: > Hello, > > it seems that the KGS bot tournament did not start, yet. > What is the matter? The tournament was played, I am not sure why the standings did not update. If I'm reading the game histories correctly: 1. Zen7 pts 2. Leela 4 pts 3. Aya

Re: [Computer-go] July KGS bot tournament

On 8/07/2017 9:07, Nick Wedd wrote: > The July KGS bot tournament will be on Sunday, July 7th, starting at > 08:00 UTC and end by 15:00 UTC. It will use 19x19 boards, with > time limits of 14 minutes each and very fast Canadian overtime, and > komi of 7½. It will be a Swiss

Re: [Computer-go] Value network that doesn't want to learn.

On 19-06-17 17:38, Vincent Richard wrote: > During my research, I’ve trained a lot of different networks, first on > 9x9 then on 19x19, and as far as I remember all the nets I’ve worked > with learned quickly (especially during the first batches), except the > value net which has always been

Re: [Computer-go] mini-max with Policy and Value network

On 24-05-17 05:33, "Ingo Althöfer" wrote: >> So, 0.001% probability. Demis commented that Lee Sedol's winning move in >> game 4 was a one in 10 000 move. This is a 1 in 100 000 move. > > In Summer 2016 I checked the games of AlphaGo vs Lee Sedol > with repeated runs of CrazyStone DL: > In 3 of 20

[Computer-go] Xeon Phi result

Hi all, I managed to get a benchmark off of a Intel® Xeon Phi™ Processor 7250 16GB, 1.40 GHz, 68 core (272 thread) system. I used a version of Leela essentially identical to the public Leela 0.10.0, but compiled with -march=knl (using gcc 5.3), using an appropriate version of Intel MKL (2017.1

Re: [Computer-go] mini-max with Policy and Value network

On 23-05-17 17:19, Hideki Kato wrote: > Gian-Carlo Pascutto: <0357614a-98b8-6949-723e-e1a849c75...@sjeng.org>: > >> Now, even the original AlphaGo played moves that surprised human pros >> and were contrary to established sequences. So where did those come >>

Re: [Computer-go] mini-max with Policy and Value network

On 23-05-17 10:51, Hideki Kato wrote: > (2) The number of possible positions (input of the value net) in > real games is at least 10^30 (10^170 in theory). If the value > net can recognize all? L depend on very small difference of > the placement of stones or liberties. Can we provide

Re: [Computer-go] mini-max with Policy and Value network

On 22-05-17 21:01, Marc Landgraf wrote: > But what you should really look at here is Leelas evaluation of the game. Note that this is completely irrelevant for the discussion about tactical holes and the position I posted. You could literally plug any evaluation into it (save for a static oracle,

Re: [Computer-go] mini-max with Policy and Value network

On 23-05-17 03:39, David Wu wrote: > Leela playouts are definitely extremely bad compared to competitors like > Crazystone. The deep-learning version of Crazystone has no value net as > far as I know, only a policy net, which means it's going on MC playouts > alone to produce its evaluations.

Re: [Computer-go] mini-max with Policy and Value network

On 22-05-17 17:47, Erik van der Werf wrote: > On Mon, May 22, 2017 at 3:56 PM, Gian-Carlo Pascutto <g...@sjeng.org > <mailto:g...@sjeng.org>> wrote: > > Well, I think that's fundamental; you can't be wide and deep at the same > time, but at least you can chose an

Re: [Computer-go] mini-max with Policy and Value network

On 22-05-17 15:46, Erik van der Werf wrote: > Oh, haha, after reading Brian's post I guess I misunderstood :-) > > Anyway, LMR seems like a good idea, but last time I tried it (in Migos) > it did not help. In Magog I had some good results with fractional depth > reductions (like in Realization

Re: [Computer-go] mini-max with Policy and Value network

On 22-05-17 14:48, Brian Sheppard via Computer-go wrote: > My reaction was "well, if you are using alpha-beta, then at least use > LMR rather than hard pruning." Your reaction is "don't use > alpha-beta", and you would know better than anyone! There's 2 aspects to my answer: 1) Unless you've

Re: [Computer-go] mini-max with Policy and Value network

On 22-05-17 11:27, Erik van der Werf wrote: > On Mon, May 22, 2017 at 10:08 AM, Gian-Carlo Pascutto <g...@sjeng.org > <mailto:g...@sjeng.org>> wrote: > > ... This heavy pruning > by the policy network OTOH seems to be an issue for me. My program has >

Re: [Computer-go] mini-max with Policy and Value network

On 20/05/2017 22:26, Brian Sheppard via Computer-go wrote: > Could use late-move reductions to eliminate the hard pruning. Given > the accuracy rate of the policy network, I would guess that even move > 2 should be reduced. > The question I always ask is: what's the real difference between MCTS

Re: [Computer-go] Patterns and bad shape

On 17-04-17 15:04, David Wu wrote: > If you want an example of this actually mattering, here's example where > Leela makes a big mistake in a game that I think is due to this kind of > issue. Ladders have specific treatment in the engine (which also has both known limitations and actual bugs in

Re: [Computer-go] Zen lost to Mi Yu Ting

On 22-03-17 16:27, Darren Cook wrote: > (Japanese rules are not *that* hard. IIRC, Many Faces, and all other > programs, including my own, scored in them There is a huge difference between doing some variation of territory scoring and implementing Japanese rules. Understanding this difference

Re: [Computer-go] Zen lost to Mi Yu Ting

On 22-03-17 09:41, Darren Cook wrote: >> The issue with Japanese rules is easily solved by refusing to play >> under ridiculous rules. Yes, I do have strong opinions. :) > > And the problem with driver-less cars is easily "solved" by banning > all road users that are not also driver-less cars

Re: [Computer-go] Zen lost to Mi Yu Ting

On 22-03-17 00:36, cazen...@ai.univ-paris8.fr wrote: > > Why can't you reuse the same self played games but score them If you have self-play games that are played to the final position so scoring is fool-proof, then it could work. But I think things get really interesting when timing of a pass

Re: [Computer-go] Zen lost to Mi Yu Ting

On 21/03/2017 21:08, David Ongaro wrote: >> But how would you fix it? Isn't that you'd need to retrain your value >> network from the scratch? > > I would think so as well. But I some months ago I already made a > proposal in this list to mitigate that problem: instead of training a > different

[Computer-go] AMD Ryzen benchmarks for Go

Linux 4.10.1 (has SMT scheduler fix) GCC 5.4 - so no Ryzen optimizations pachi-git-13115394 Intel Haswell t=8 13325 g/s t=1 1665 g/s @3.6GHz t=49352 g/s t=1 2338 g/s @3.6GHz t=12542 g/s@3.8GHz AMD Ryzen t=16 26589 g/s t=1 1661 g/s @3.7GHz t=8 15464 g/s t=1 1933

Re: [Computer-go] New AMD processors

On 03-03-17 21:29, "Ingo Althöfer" wrote: > Hi, > > AMD has published a new (fast and cool) processor, the Ryzen. > Did some go programmers already collect experiences with it? > Do they combine well with GPUs? I'm not getting one until there are mainboard reviews out, because there seem to be

Re: [Computer-go] UEC wild cards?

On 21/02/2017 16:11, "Ingo Althöfer" wrote: > Dear UEC organizers, > > GCP wrote (on behalf of Leela): >> I did not register for the UEC Cup. I seem to be in good company there, >> sadly. > > do you have a few wild cards for strong late entries? Posting on behalf of the UEC organizers: Yes,

Re: [Computer-go] Leela Superstar!

On 21-02-17 16:27, Aja Huang via Computer-go wrote: > Congrats for Leela's significant improvements. :) Thank you. When I said I was "in good company" by not having registered for the UEC Cup, I was actually referring to you (AlphaGo), BTW. I feel that maybe Ingo may have misunderstood me there

Re: [Computer-go] Leela Superstar!

On 19-02-17 17:00, "Ingo Althöfer" wrote: > Hi, > the rank graph of LeelaX on KGS looks impressive: > > http://www.dgob.de/yabbse/index.php?action=dlattach;topic=6048.0;attach=5658;image > > Of course, its shape will be more "gnubbled" after a few days. Thank you for the kind words, it is

Re: [Computer-go] Playout policy optimization

On 12/02/2017 5:44, Álvaro Begué wrote: > I thought about this for about an hour this morning, and this is what I > came up with. You could make a database of positions with a label > indicating the result (perhaps from real games, perhaps similarly to how > AlphaGo trained their value network).

Re: [Computer-go] AlphaGo rollout nakade patterns?

On 31-01-17 16:32, Roel van Engelen wrote: > @Brain Sheppard > Thanks that is a really useful explanation! > the way you state: "and therefore a 8192-sized pattern set will identify > all potential nakade." seems to indicate this is a known pattern set? > could i find some more information on it

Re: [Computer-go] AlphaGo rollout nakade patterns?

On 23-01-17 20:10, Brian Sheppard via Computer-go wrote: > only captures of up to 9 stones can be nakade. I don't really understand this. http://senseis.xmp.net/?StraightThree Both constructing this shape and playing the vital point are not captures. How can you detect the nakade (and play at a

Re: [Computer-go] Messages classified as spam.

On 12/01/2017 11:55, Rémi Coulom wrote: > It is the mail server of this mailing list that is not well > configured. Even my own messages are classified as spam for me now. > The list does not send DKIM identification. It's been a while since I looked at this in depth, but the problem seems to be

Re: [Computer-go] Training the value network (a possibly more efficient approach)

On 11-01-17 18:09, Xavier Combelle wrote: > Of course it means distribute at least the binary so, or the source, > so proprietary software could be reluctant to share it. But for free > software there should not any problem. If someone is interested by my > proposition, I would be pleased to

Re: [Computer-go] Computer-go - Simultaneous policy and value functions reinforcement learning by MCTS-TD-Lambda ?

Patrick, for what it's worth, I think almost no-one will have seen your email because laposte.net claims it's forged. Either your or laposte.net's email server is mis-configured. > Refering to Silver's paper terminology and results, greedy policy > using RL Policy Network beated greedy policy

Re: [Computer-go] Training the value network (a possibly more efficient approach)

On 10-01-17 23:25, Bo Peng wrote: > Hi everyone. It occurs to me there might be a more efficient method to > train the value network directly (without using the policy network). > > You are welcome to check my > method: http://withablink.com/GoValueFunction.pdf > For Method 1 you state:

Re: [Computer-go] Training the value network (a possibly more efficient approach)

On 11-01-17 14:33, Kensuke Matsuzaki wrote: > Hi, > > I couldn't get positive experiment results on Ray. > > Rn's network structure of V and W are similar and share parameters, > but only final convolutional layer are different. > I trained Rn's network to minimize MSE of V(s) + W(s). > It uses

Re: [Computer-go] Golois5 is KGS 4d

On 10-01-17 15:05, Hiroshi Yamashita wrote: > Hi, > > Golois5 is KGS 4d. > I think it is a first bot that gets 4d by using DCNN without search. I found this paper: https://openreview.net/pdf?id=Bk67W4Yxl They are using residual layers in the DCNN. -- GCP

Re: [Computer-go] GTX 1080 benchmark

On 15/12/2016 12:35, Hiroshi Yamashita wrote: > F32F128F256MNIST > GTX 1080 0.48ms 1.45ms 2.38ms 17sec, CUDA 8.0, cuDNN v5.0, Core i7 > 980X 3.3GHz 6core > GTX 1080 0.87ms 1.79ms 2.65ms 19sec, CUDA 8.0, cuDNN v5.1, Core i7 > 980X 3.3GHz 6core > GTX 980 0.60ms

Re: [Computer-go] Aya reaches pro level on GoQuest 9x9 and 13x13

On 17-11-16 22:38, Hiroshi Yamashita wrote: > Value Net is 32 Filters, 14 Layers. > 32 5x5 x1, 32 3x3 x11, 32 1x1 x1, fully connect 256, fully connect tanh 1 I think this should be: 32 5x5 x1, 32 3x3 x11, 1 1x1 x1, fully connect 256, fully connect tanh 1 Else one has a 361 * 32 * 256 layer with

Re: [Computer-go] Aya reaches pro level on GoQuest 9x9 and 13x13

On 20-11-16 11:16, Detlef Schmicker wrote: > Hi Hiroshi, > >> Now I'm making 13x13 selfplay games like AlphaGo paper. 1. make a >> position by Policy(SL) probability from initial position. 2. play a >> move uniformly at random from available moves. 3. play left moves >> by Policy(RL) to the end.

Re: [Computer-go] Aya reaches pro level on GoQuest 9x9 and 13x13

On 17/11/2016 22:38, Hiroshi Yamashita wrote: > Features are 49 channels. > http://computer-go.org/pipermail/computer-go/2016-February/008606.html ... > Value Net is 32 Filters, 14 Layers. > 32 5x5 x1, 32 3x3 x11, 32 1x1 x1, fully connect 256, fully connect tanh 1 > Features are 50 channels. >

Re: [Computer-go] Time policy

On 04-11-16 04:45, Billy White wrote: > Hi, > > Our team is working on a computer go system mainly followed alphago. > We try to add time policy to our system but cannot find something > useful. > > I am wondering whether there are some useful material? Take a large games database, and

Re: [Computer-go] DarkForest policy network training code is open-source now.

On 04-10-16 23:47, Yuandong Tian wrote: > Hi all, > > DarkForest training code is open source now. Hopefully it will help the > community. > > https://github.com/facebookresearch/darkforestGo > > > With 4 GPUs, the training procedure gives

Re: [Computer-go] Congratulations to AyaMC!

On 7/09/2016 21:21, Nick Wedd wrote: > Congratulations to AyaMC, undefeated winner of the September slow KGS > bot tournament, which ended earlier today! > > My report is at http://www.weddslist.com/kgs/past/S16.2/index.html > As usual, I will welcome your comments and corrections. Given that

Re: [Computer-go] Converging to 57%

On 23/08/2016 11:26, Brian Sheppard wrote: > The learning rate seems much too high. My experience (which is from > backgammon rather than Go, among other caveats) is that you need tiny > learning rates. Tiny, as in 1/TrainingSetSize. I think that's overkill, as in you effectively end up doing

Re: [Computer-go] Converging to 57%

On 23-08-16 08:57, Detlef Schmicker wrote: > So, if somebody is sure, it is measured against GoGod, I think a > number of other go programmers have to think again. I heard them > reaching 51% (e. g. posts by Hiroshi in this list) I trained a 128 x 14 network for Leela 0.7.0 and this gets 51.1%

Re: [Computer-go] Congratulations to Zen!

On 17/07/2016 17:03, Xavier Combelle wrote: > It seems that on my firefox 47.0.1 some html entities are rendered for > example komi 7 but with the 9times;9 tournament still running, Renders correctly here and also with a clean Firefox profile, so the problem is likely either your

Re: [Computer-go] DarkForest is open-source now.

On 10/06/2016 19:57, Darren Cook wrote: > At 5d KGS, is this the world's strongest MIT/BSD licensed program? ... > actually, is there any other MIT/BSD go program out there? (I thought > Pachi was, but it is GPLv2) Huh, that's interesting, because Darkforest seems to have copy-pasted the pachi

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