Re: [Computer-go] Training an AlphaGo Zero-like algorithm with limited hardware on 7x7 boards

To be clear, what I was talking about was building an opening book as part of the game-generation process that produces training data for the neural network. This makes sure you don't generate the same game over and over again. A few more things about my Spanish checkers experiment from a few

Re: [Computer-go] Training an AlphaGo Zero-like algorithm with limited hardware on 7x7 boards

For checkers, I used a naive implementation of UCT as my opening book (the "playout" being the actual game where the engine is thinking). So towards the end of the opening book there is always a position where it will try a random move, but in the long run good opening moves will be explored more

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

From before AlphaGo was announced, I thought the way forward was generating games that play to the bitter end maximizing score, and then using the final ownership as something to predict. I am very glad that someone has had the time to put this idea (and many others!) into practice.

Re: [Computer-go] Paper “Complexity of Go” by Robson

...or you could just not get your knickers in a twist over somebody's pronoun selection. I am here for the discussions about computer go, not gender politics. On Thu, Jun 21, 2018 at 6:24 PM, Mario Xerxes Castelán Castro wrote: > “He” is the genetic singular pronoun in English. If anybody

Re: [Computer-go] Paper “Complexity of Go” by Robson

I don't think ko fights have anything to do with this. John Tromp told me that ladders are PSPACE complete: https://tromp.github.io/lad.ps Álvaro. On Mon, Jun 18, 2018 at 2:58 PM, uurtamo wrote: > FWIW, first-capture go (i.e. winner is first one to make a capture) should > not be

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

Sorry, I haven't been paying enough attention lately to know what "alpha-beta rollouts" means precisely. Can you either describe them or give me a reference? Thanks, Álvaro. On Tue, Mar 6, 2018 at 1:49 PM, Dan wrote: > I did a quick test with my MCTS chess engine wth two

Re: [Computer-go] Crazy Stone is back

> I tried chain pooling too, and it was too slow. It made the network about twice slower in tensorflow (using tf.unsorted_segment_sum or max). I'd rather have twice more layers. tf.unsorted_segment_max didn't exist in the first public release of TensorFlow, so I requested it just for this purpose

Re: [Computer-go] MiniGo open sourced

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 the advent of strong open-source programs.

Re: [Computer-go] Art or not?

I have a personal definition of art that works pretty well: Pretentious entertainment. Emphasis on “pretentious”. On a more serious note, I don’t care if anything I produce is art or not, and neither should you. If you enjoy what you are doing, keep it up! Álvaro. On Tuesday, December 5, 2017,

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

). b The leaf node is > expanded..." > > > > > > > 2017-12-03 9:44 GMT-06:00 Álvaro Begué <alvaro.be...@gmail.com>: > >> I am not sure where in the paper you think they use Q(s,a) for a node s >> that hasn't been expanded yet. Q(s,a

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

I am not sure where in the paper you think they use Q(s,a) for a node s that hasn't been expanded yet. Q(s,a) is a property of an edge of the graph. At a leaf they only use the `value' output of the neural network. If this doesn't match your understanding of the paper, please point to the

Re: [Computer-go] Learning related stuff

The term you are looking for is "transfer learning": https://en.wikipedia.org/wiki/Transfer_learning On Tue, Nov 21, 2017 at 5:27 PM, "Ingo Althöfer" <3-hirn-ver...@gmx.de> wrote: > Hi Erik, > > > No need for AlphaGo hardware to find out; any > > toy problem will suffice to explore different >

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

It's a model written using the Keras neural network library: https://en.wikipedia.org/wiki/Keras On Fri, Nov 10, 2017 at 7:09 AM, Xavier Combelle wrote: > You make me really curious, > what is a Keras model ? > > Le 10/11/2017 à 01:47, Petr Baudis a écrit : > > Hi,

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

Your understanding matches mine. My guess is that they had a temperature parameter in the code that would allow for things like slowly transitioning from random sampling to deterministically picking the maximum, but they ended up using only those particular values. Álvaro. On Tue, Nov 7, 2017

Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

p) to > determine the utility function, which is why having a continuous log of all > the input streams is necessary. > > On Oct 30, 2017, 3:45 PM -0700, Álvaro Begué <alvaro.be...@gmail.com>, > wrote: > > In your hypothetical scenario, if the car can give you as mu

Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

In your hypothetical scenario, if the car can give you as much debugging information as you suggest (100% tree is there, 95% child is there), you can actually figure out what's happening. The only other piece of information you need is the configured utility values for the possible outcomes. Say

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

There are ways to do it, but it might be messy. However, the vast majority of the computational effort will be in playing games to generate a training database, and that part is trivial to distribute. Testing if the new version is better than the old version is also very easy to distribute.

Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

No, they are too few games for that. On Mon, Oct 23, 2017 at 8:05 AM, Jim O'Flaherty wrote: > Couldn't they be useful as part of a set of training data for newly > trained engines and networks? > > On Oct 23, 2017 2:34 AM, "Petri Pitkanen"

Re: [Computer-go] Zero performance

I suggest scaling down the problem until some experience is gained. You don't need the full-fledge 40-block network to get started. You can probably get away with using only 20 blocks and maybe 128 features (from 256). That should save you about a factor of 8, plus you can use larger

Re: [Computer-go] AlphaGo Zero

When I did something like this for Spanish checkers (training a neural network to be the evaluation function in an alpha-beta search, without any human knowledge), I solved the problem of adding game variety by using UCT for the opening moves. That means that I kept a tree structure with the

Re: [Computer-go] AlphaGo Zero

Yes, residual networks are awesome! I learned about them at ICML 2016 ( http://kaiminghe.com/icml16tutorial/index.html). Kaiming He's exposition was fantastically clear. I used them in my own attempts at training neural networks for move prediction. It's fairly easy to train something with 20

Re: [Computer-go] AlphaGo Zero

Yes, it seems really odd that they didn't add a plane of all ones. The "heads" have weights that depend on the location of the board, but all the other layers can't tell the difference between a lonely stone at (1,1) and one at (3,3). In my own experiments (trying to predict human moves) I found

Re: [Computer-go] AlphaGo Zero

This is a quick check of my understanding of the network architecture. Let's count the number of parameters in the model: * convolutional block: (17*9+1)*256 + 2*256 [ 17 = number of input channels 9 = size of the 3x3 convolution window 1 = bias (I am not sure this is needed if you are

Re: [Computer-go] AlphaGo Zero

A link to the paper (from the blog post): https://deepmind.com/documents/119/agz_unformatted_nature.pdf Enjoy! Álvaro. On Wed, Oct 18, 2017 at 2:29 PM, Richard Lorentz wrote: > Wow! That's very exciting. I'm glad they didn't completely shelve the > project as they

Re: [Computer-go] Neural nets for Go - chain pooling?

When TensorFlow was first released I used it to implement a CNN for move prediction and evaluation, and I requested the addition of a function to implement chain pooling: https://github.com/tensorflow/tensorflow/issues/549 It's now implemented here:

Re: [Computer-go] Alphago and solving Go

Eventually exploring the entire tree is what I would call "mathematically sound", meaning that given enough time the algorithm is guaranteed to play optimally. I would reserve "brute force" for algorithms that simply search every possible variant exhaustively, like John Tromp's connect 4 program

Re: [Computer-go] Alphago and solving Go

No, it is not possible to solve go on a 19x19 board. The closest we have is 5x5, I believe. We have a pretty good idea what optimal play looks like on 7x7. The difficulty of finding optimal play on large boards is unfathomable. Álvaro. On Sun, Aug 6, 2017 at 10:06 AM Cai Gengyang

Re: [Computer-go] purpose of zero-filled feature planes in CNN

I agree with you. It makes no sense. You'll take whatever linear combinations you want and they'll all be zero. Álvaro. On Tue, Jul 18, 2017 at 6:53 AM, Brian Lee wrote: > I've been wondering about something I've seen in a few papers (AlphaGo's > paper, Cazenave's

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

On Tue, May 23, 2017 at 4:51 AM, Hideki Kato wrote: > (3) CNN cannot learn exclusive-or function due to the ReLU > activation function, instead of traditional sigmoid (tangent > hyperbolic). CNN is good at approximating continuous (analog) > functions but Boolean

Re: [Computer-go] What was the final score after the counting of AlphaGo-vs-Ke Jie Game #1?

AlphaGo as white won by 0.5 points. On Tue, May 23, 2017 at 3:00 AM, Jim O'Flaherty wrote: > The announcer didn't have her mic on, so I couldn't hear the final score > announced... > > So, what was the final score after the counting of AlphaGo-vs-Ke Jie Game > #1?

Re: [Computer-go] Aya's selfplay games for training value network

nd to play bad move, like self-atari, filling eye, breaking seki, > if those moves do not change the result. > So it is maybe not good at ownership map. > > Thanks, > Hiroshi Yamashita > > > - Original Message - From: "Álvaro Begué" <alvaro.be...@gmail.com>

Re: [Computer-go] Regarding MCTS playouts

For identifying points that look like eyes, it's useful to have a 16-bit value at each position of the board that contains the colors of the 8 neighbors (2 bits per neighbor, with an encoding like 00=empty, 01=black, 10=white, 11=outside). You can maintain this incrementally when a point on the

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

Thank you, Gian-Carlo. I couldn't have said it better. Álvaro. On Wed, Mar 22, 2017 at 7:07 AM, Gian-Carlo Pascutto wrote: > 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

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

I was thinking the same thing. You can easily equip the value network with several outputs, corresponding to several settings of komi, then train as usual. The issue with Japanese rules is easily solved by refusing to play under ridiculous rules. Yes, I do have strong opinions. :) Álvaro. On

Re: [Computer-go] New AMD processors

Oh, you are using a value net? How did you train it? I don't see anything about it in the bitbucket repository... Álvaro. P.S.- Sorry about the thread hijacking, everyone. On Sat, Mar 4, 2017 at 4:29 AM, Detlef Schmicker wrote: > I looked into this too: > > oakfoam would not

Re: [Computer-go] dealing with multiple local optima

I should point out that Reinforcement Learning is a relatively unimportant part of AlphaGo, according to the paper. They only used it to turn the move-prediction network into a stronger player (presumably increasing the weights of the layer before SoftMax would do most of the job, by making the

[Computer-go] Fwd: Playout policy optimization

Thanks, Rémi! -- Forwarded message -- From: Rémi Coulom <remi.cou...@free.fr> Date: Sun, Feb 12, 2017 at 4:24 AM Subject: Playout policy optimization To: Álvaro Begué <alvaro.be...@gmail.com> Hi Alvaro, I cannot post to the list any more. Please forward this message

[Computer-go] Playout policy optimization

Hi, I remember an old paper by Rémi Coulom ("Computing Elo Ratings of Move Patterns in the Game of Go") where he computed "gammas" (exponentials of scores that you could feed to a softmax) for different move features, which he fit to best explain the move probabilities from real games.

Re: [Computer-go] Notes from the Asilomar Conference on Beneficial AI

If you like video commentary, Haylee has five game reviews, starting with this one: https://www.youtube.com/watch?v=b_24iaUMRFs=1109s You may also enjoy this lecture (probably best for kyu players): https://www.youtube.com/watch?v=v8Eh41m7gVA (you may want to skip to around 9:00). Enjoy, Álvaro.

Re: [Computer-go] Our Silicon Overlord

If you are killed by an AI-driven car, the manufacturer will use the case to improve the algorithm and make sure that this type of death never happens again. Unfortunately a death by a drunk driver doesn't seem to teach anyone anything and will keep happening as long as people need to drive and

Re: [Computer-go] Some experiences with CNN trained on moves by the winning player

On Sun, Dec 11, 2016 at 4:50 PM, Rémi Coulom wrote: > It makes the policy stronger because it makes it more deterministic. The > greedy policy is way stronger than the probability distribution. > I suspected this is what it was mainly about. Did you run any experiments to

Re: [Computer-go] Time policy

Start by computing a "normal" amount of time to spend, using the kinds of rules described by others in this thread. Since you are using MCTS, you may want to experiment with spending more time if the move with the best score is not the one that has been explored the most, since that probably

Re: [Computer-go] Having an "estimated winrate" on the AGA pro game broadcasts

One could use a curve to map the MC winning rate to an actual winning probability. It would take only thousands of games to learn such a curve (as opposed to the 30 million games used to train the value network in AlphaGo). Álvaro. On Wed, Aug 31, 2016 at 8:24 PM, Dan Schmidt

Re: [Computer-go] Converging to 57%

There are situations where carefully crafting the minibatches makes sense. For instance, if you are training an image classifier it is good to build the minibatches so the classes are evenly represented. In the case of predicting the next move in go I don't expect this kind of thing will make much

Re: [Computer-go] Creating the playout NN

I don't understand the point of using the deeper network to train the shallower one. If you had enough data to be able to train a model with many parameters, you have enough to train a model with fewer parameters. Álvaro. On Sun, Jun 12, 2016 at 5:52 AM, Michael Markefka <

Re: [Computer-go] Hajin Lee will play a live commented game against CrazyStone

Disclaimer: I haven't actually implemented MCTS with NNs, but I have played around with both techniques. Would it make sense to artificially scale down the values before the SoftMax is applied, so the probability distribution is not as concentrated, and unlikely moves are not penalized as much?

Re: [Computer-go] Hajin Lee will play a live commented game against CrazyStone

I just saw the video here: https://www.youtube.com/watch?v=ZdrV2H5zIOM It's fun to hear the pro making comments as she goes. I had hoped for a better game, though. Any comments from the CS camp? Thanks, Álvaro. On Mon, May 16, 2016 at 3:58 AM, Xavier Combelle

Re: [Computer-go] Machine for Deep Neural Net training

What are you doing that uses so much disk space? An extremely naive computation of required space for what you are doing is: 30M samples * (42 input planes + 1 output plane)/sample * 19*19 floats/plane * 4 bytes/float = 1.7 TB So that's cutting it close, But I think the inputs and outputs are all

Re: [Computer-go] Beginner question : how to choose a board representation

Hi, I also did computer chess before go (and checkers before chess). I would start with a straight-forward implementation and learn with it. If you end up finding your board representation limiting, rewrite it. Here's some code from my program: int const N = 19; int const XN = N + 2; int const

Re: [Computer-go] new challenge for Go programmers

A very simple-minded way of trying to identify what a particular neuron in the upper layers is doing is to find the 50 positions in the database that make it produce the highest activation values. If the neuron is in one of the convolutional layers, you get a full 19x19 image of activation values,

Re: [Computer-go] new challenge for Go programmers

> no lack of respect for DeepMind's achievement was contained in my > posting; on the contrary, i was as surprised as anyone at how well she > did and it gave me great pause for thought. > Well, you wrote this: > but convolutional neural networks and monte-carlo simulators have not > advanced

Re: [Computer-go] UEC cup 2nd day

I have used TensorFlow to train a CNN that predicts the next move, with a similar architecture to what others have used (1 layers of 5x5 convolutions followed by 10 more layers of 3x3 convolutions, with 192 hidden units per layer and ReLU activation functions) but with much simpler inputs. I found

Re: [Computer-go] Congratulations to AlphaGo (Statistical significance of results)

On Tue, Mar 22, 2016 at 1:40 PM, Nick Wedd <mapr...@gmail.com> wrote: > On 22 March 2016 at 17:20, Álvaro Begué <alvaro.be...@gmail.com> wrote: > >> A very simple-minded analysis is that, if the null hypothesis is that >> AlphaGo and Lee Sedol are equally strong,

Re: [Computer-go] Congratulations to AlphaGo (Statistical significance of results)

A very simple-minded analysis is that, if the null hypothesis is that AlphaGo and Lee Sedol are equally strong, AlphaGo would do as well as we observed or better 15.625% of the time. That's a p-value that even social scientists don't get excited about. :) Álvaro. On Tue, Mar 22, 2016 at 12:48

Re: [Computer-go] Go Bot for the Browser?

Actually the DCNN plays on 9x9 acceptably well (somewhere in the single-digit kyus). On Friday, March 18, 2016, Benjamin Teuber wrote: > This is really cool. Now it just needs to learn 9x9 via reinforcement > learning ;-) > > Josef Moudrik

Re: [Computer-go] Go Bot for the Browser?

A while back somebody posted a link to a browser implementation of a DCNN: https://chrisc36.github.io/deep-go/ Would something like that do? Álvaro. On Wed, Mar 16, 2016 at 4:44 PM, Benjamin Teuber wrote: > Hi everyone, > > for a Go beginner website I would like to have

Re: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points?

I have experimented with a CNN that predicts ownership, but I found it to be too weak to be useful. The main difference between what Google did and what I did is in the dataset used for training: I had tens of thousands of games (I did several different experiments) and I used all the positions

Re: [Computer-go] Frisbee Go

You could express the intended move as a pair of real numbers. A random offset is then added, following some probability distribution (Gaussian, or uniform in a disk of a certain radius, or ...), and then the result is rounded to the nearest point of integer coordinates. What possibilities does

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

I am not sure how exactly they define MSE. If you look at the plot in figure 2b, the MSE at the very beginning of the game (where you can't possibly know anything about the result) is 0.50. That suggests it's something else than your [very sensible] interpretation. Álvaro. On Thu, Feb 4, 2016

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

The positions they used are not from high-quality games. They actually include one last move that is completely random. Álvaro. On Thursday, February 4, 2016, Detlef Schmicker wrote: > -BEGIN PGP SIGNED MESSAGE- > Hash: SHA1 > > Hi, > > I try to reproduce numbers from

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

efer to the finaly used data set: > in the referred chapter they state, they have used their kgs dataset > in a first try (which is in another part of the paper referred to > being a 6d+ data set). > > Am 04.02.2016 um 18:11 schrieb Álvaro Begué: > > The positions th

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

I just want to see how to get 0.5 for the initial position on the board with some definition. One possibility is that 0=loss, 1=win, and the number they are quoting is sqrt(average((prediction-outcome)^2)). On Thu, Feb 4, 2016 at 3:40 PM, Hideki Kato wrote: > I think

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

I searched for the file name on the web and found this copy: http://airesearch.com/wp-content/uploads/2016/01/deepmind-mastering-go.pdf Álvaro. On Wed, Feb 3, 2016 at 4:37 AM, Oliver Lewis wrote: > Is the paper still available for download? The direct link appears to be

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

Aja, I read the paper with great interest. [Insert appropriate praises here.] I am trying to understand the part where you use reinforcement learning to improve upon the CNN trained by imitating humans. One thing that is not explained is how to determine that a game is over, particularly when a

Re: [Computer-go] AlphaGo MCTS & Reinforcement Learning?

How about you read the paper first? The conversation would make much more sense if you actually spent some time trying to understand the details of what they did. :) <-- (mandatory smiley to indicate I am not upset or anything) On Sun, Jan 31, 2016 at 10:20 AM, Greg Schmidt

Re: [Computer-go] Game Over

It's in the paper: "ladder capture" and "ladder escape" are features that are fed as inputs into the CNN. Álvaro. On Wed, Jan 27, 2016 at 6:03 PM, Ryan Grant wrote: > To the authors: Did the deep-NN architecture learn ladders on its own, > or was any extra

Re: [Computer-go] Facebook Go

Yes, it has been: http://computer-go.org/pipermail/computer-go/2015-November/008267.html Are there any news on Google's efforts? Álvaro. On Wed, Jan 27, 2016 at 10:10 AM, Richard Lorentz wrote: > Not sure if this has been posted here already or not: >

Re: [Computer-go] Board evaluation using a convolutional neural network

I understand that using games from humans to learn about life and death introduces all sorts of biases. That's why I tried to use games from an engine instead. In their standard configuration, MCTS engines will sometimes let lots of groups die after they know the game is hopeless, or if they have

Re: [Computer-go] Board evaluation using a convolutional neural network

16 at 5:14 PM, Petr Baudis <pa...@ucw.cz> wrote: > Hi! > > On Fri, Jan 15, 2016 at 04:54:18PM -0500, Álvaro Begué wrote: > > In their standard configuration, MCTS engines will sometimes let lots of > > groups die after they know the game is hopeless, or if they have a la

Re: [Computer-go] Board evaluation using a convolutional neural network

> Regarding 9x9, I believe Alvaro Begue has explored this idea in a way > which perhaps would work better in a go engine. He used pachi to generate a > database of games by playing against itself and then trained a model in a > similar fashion to what I did. I'm not sure about the results of his >

Re: [Computer-go] CNN with 54% prediction on KGS 6d+ data

I don't think the CPU-GPU communication is what's going to kill this idea. The latency in actually computing the feed-forward pass of the CNN is going to be in the order of 0.1 seconds (I am guessing here), which means finishing the first playout will take many seconds. So perhaps it would be

Re: [Computer-go] CNN with 54% prediction on KGS 6d+ data

one forget the fact that stronger playouts don't necessarily lead > to an better evaluation function? (Yes, that what playouts essential are, a > dynamic evaluation function.) This is even under the assumption that we can > reach the same number of playouts per move. > > > O

Re: [Computer-go] Strong engine that maximizes score

Thanks for your answer. Unfortunately Pachi doesn't seem to really try to maximize score, even with these settings: Once one side has won by a large enough margin, it will stop trying to kill small groups and I am precisely trying to generate a database to learn about life and death. Perhaps I

Re: [Computer-go] Strong engine that maximizes score

After reading the relevant code, I realized that val_scale=1.0 should do precisely what I wanted. I have tested it a bit, and so far so good. Thanks! Álvaro. On Tue, Nov 17, 2015 at 7:12 AM, Petr Baudis <pa...@ucw.cz> wrote: > Hi! > > On Tue, Nov 17, 2015 at 07:05:34AM -050

Re: [Computer-go] Strong engine that maximizes score

I wouldn't say they are "not compatible", since the move that maximizes score is always in the top class (win>draw>loss) for any setting of komi. You probably mean it in a practical sense, in that MCTS engines are stronger when maximizing win probability. I am more interested in attempting to

[Computer-go] Strong engine that maximizes score

Hi, I am trying to create a database of games to do some machine-learning experiments. My requirements are: * that all games be played by the same strong engine on both sides, * that all games be played to the bitter end (so everything on the board is alive at the end), and * that both sides

Re: [Computer-go] Frisbee Go Simulation

Normalizing the probabilities and re-throwing the frisbee until it lands in a valid move are equivalent, of course. On Thu, Nov 12, 2015 at 5:01 AM, David Peters wrote: > To keep changes to the protocol and number of parameters low, wouldn't it > be a possibility to consider

Re: [Computer-go] Frisbee Go Simulation

1/5 also seems natural (equal chance of hitting each the 5 possible points). Álvaro. On Wed, Nov 11, 2015 at 10:08 AM, John Tromp wrote: > > By the way: It would also be necessary to decide about > > the eps for the event. Natural candidates would be > > eps=0.1 or

Re: [Computer-go] Frisbee Go Simulation

Oh! You can have a continuous handicap control by giving the players different epsilons. :) On Wed, Nov 11, 2015 at 2:25 PM, John Tromp wrote: > >> Would the game end after two unintentional passes? > > > Good point. In principle I would say so. > > That makes little

Re: [Computer-go] Number of 3x3 patterns

I get 1107 (954 in the middle + 135 on the edge + 18 on a corner). Álvaro. On Tue, Nov 3, 2015 at 2:00 PM, Detlef Schmicker wrote: > -BEGIN PGP SIGNED MESSAGE- > Hash: SHA1 > > Thanks, but I need them reduced by reflection and rotation symmetries > (and leave the

Re: [Computer-go] How to handle triple ko efficiently?

> Btw does anyone have a good initialization vector for the Zobrist table? The obvious thing to try is random numbers. Another idea is turning your Zobrist key into CRC64, which I think is what you get if you generate your numbers like this: #include int main() { unsigned long long const P =

Re: [Computer-go] How to handle triple ko efficiently?

nterested would be ensuring each bit has a similar ratio of occurrences, > or bruteforcing it. > > On 16/10/2015 14:51, Álvaro Begué wrote: > >> Btw does anyone have a good initialization vector for the Zobrist table? >>> >> The obvious thing to try is random numbers. Anothe

Re: [Computer-go] How to handle triple ko efficiently?

amm...@gmail.com> wrote: > If you're only getting 1000 table generations a second, you should look > into your algorithm. You should get at least 100,000 table generations a > second! > > > On 2015-10-16 7:21, Álvaro Begué wrote: > > That sounds kind of obsessive. I think

Re: [Computer-go] what do you (or does your computer) see?

Could you please stop posting your videos to this list? I find nothing of value in them. If others disagree, please speak up. Álvaro. On Thu, Sep 3, 2015 at 11:31 PM, djhbrown . wrote: > > > https://www.youtube.com/watch?v=IoO7Nhlf_k4=PL4y5WtsvtduqNW0AKlSsOdea3Hl1X_v-S=10

Re: [Computer-go] fast + good RNG

If your PRNG is consuming 40% of your CPU time, your playouts are too light. Anyway, it's very easy to make a fast PRNG these days. The first thing that comes to mind is a 64-bit linear congruential generator of which you use the middle bits, or you can XOR the high 32 bits and the low 32 bits

Re: [Computer-go] monte carlo search; all valid moves?

I am not sure I understand the question. The only thing that is typically not checked in the playouts is superko. What other validity checks are you performing? Álvaro. On Sat, Mar 28, 2015 at 9:54 AM, holger krekel hol...@merlinux.eu wrote: On Sat, Mar 28, 2015 at 08:51 +0100, folkert

Re: [Computer-go] Representing Komi for neural network

On Fri, Mar 20, 2015 at 8:24 PM, Hugh Perkins hughperk...@gmail.com wrote: On 1/12/15, Álvaro Begué alvaro.be...@gmail.com wrote: A CNN that starts with a board and returns a single number will typically have a few fully-connected layers at the end. You could make the komi an extra input

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

The human brain is not the most powerful AI, because it fails the A test. I suspect bootstrapping is not very hard. I have recently written a Spanish checkers program starting with no knowledge and I got it to play top-human level checkers within a few weeks. You can build a database of games as

Re: [Computer-go] public valid move generator algorithms?

You can keep track of pseudo-liberties, where you count a liberty multiple times if it is adjacent to multiple stones in a chain. That seems to be the easiest way to implement it, although a serious program will eventually need the actual liberty count, so perhaps you should just do that from the

Re: [Computer-go] Tromp Taylor rules http://senseis.xmp.net/?LogicalRules

Ko is not missing: It is a particular case of the prohibition to repeat positions. Making suicide illegal is an easy patch. Álvaro. On Wed, Mar 11, 2015 at 7:08 AM, folkert folk...@vanheusden.com wrote: Hi, After 3 years of not working on my Go software, I decided to tinker again a bit on

Re: [Computer-go] CNN for winrate and territory

What network architecture did you use? Can you give us some details? On Sun, Feb 8, 2015 at 5:22 AM, Detlef Schmicker d...@physik.de wrote: Hi, I am working on a CNN for winrate and territory: approach: - input 2 layers for b and w stones - 1. output: 1 layer territory (0.0 for owned

Re: [Computer-go] Representing Komi for neural network

A CNN that starts with a board and returns a single number will typically have a few fully-connected layers at the end. You could make the komi an extra input in the first one of those layers, or perhaps in each of them. Álvaro. On Sun, Jan 11, 2015 at 10:59 AM, Detlef Schmicker d...@physik.de

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional NeuralNetworks

Yes, it's 0.15 seconds for 128 positions. A minibatch is a small set of samples that is used to compute an approximation to the gradient before you take a step of gradient descent. I think it's not simply called a batch because batch training refers to computing the full gradient with all the

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

No, you don't need double precision at all. Álvaro. On Thu, Dec 25, 2014 at 5:00 AM, Detlef Schmicker d...@physik.de wrote: Hi, as I want to by graphic card for CNN: do I need double precision performance? I give caffe (http://caffe.berkeleyvision.org/) a try, and as far as I understood

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

You are going to be computing gradients of functions, and most people find it easier to think about these things using a type that roughly corresponds to the notion of real number. You can use a fixed-point representation of reals, which uses ints in the end, but then you have to worry about what

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

This is my guess as to what the number of parameters actually is: First layer: 128 * (5*5*36 + 19*19) (128 filters of size 5x5 on 36 layers of input, position-dependent biases) 11 hidden layers: 11 * 128 * (3*3*128 + 19*19) (128 filters of size 3x3 on 128 layers of input, position-dependent

[Computer-go] Building a database for training CNNs

Hi, There are things a CNN could probably do well, if only we had the right database to train it. I have in mind these two possibilities: * using a CNN as an evaluation function, * using a CNN to estimate ownership for each point (i.e., a number between -1 and 1 that is an estimate of who is

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

If you start with a 19x19 grid and you take convolutional filters of size 5x5 (as an example), you'll end up with a board of size 15x15, because a 5x5 box can be placed inside a 19x19 board in 15x15 different locations. We can get 19x19 outputs if we allow the 5x5 box to be centered on any point,

Re: [computer-go] More details about MM + source code

2009/12/13 Petr Baudis pa...@ucw.cz: On Sun, Dec 13, 2009 at 12:15:42PM +0100, Rémi Coulom wrote: If I understand correctly, you are refering to that loop: for (int i = Max; --i = Min;) It starts at Max - 1, not Max. I am pretty confident this part is correct. Otherwise, it would have

Re: [computer-go] Kinds of Zobrist hashes

2009/12/9 Christian Nentwich christ...@modeltwozero.com: On Tue, Dec 08, 2009 at 09:30:47PM -0500, Eric Boesch wrote: You can mathematically prove the two systems are almost the same, so there's no need to test.  Yes, this was my line of thought, but I wasn't sure if I'm not missing

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