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] 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] 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] 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] 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] 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] 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 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 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] 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] 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] 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

[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] 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

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] 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

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] 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

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,

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

[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] 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] 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] 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] 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 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:

[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] 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: > 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 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] 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] 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] 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 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] 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] 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] 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] 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] 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

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