these but they appear to be recent 10x128 nets, and they use a good
amount of time to play so probably don't have a playout/visit cap. I guess
I'll wait awhile to see if more LZ bots appear before running any new
10x128 nets myself.
- Andy aka KillerDucky
2018-03-04 22:38 GMT-06:00 Andy
-t2 which seems close to optimal on my machine). I have
a GTX960, when I run this test I'll post some information on how many
playouts it gets in the games.
- Andy aka KillerDucky
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Rémi, can you share any details about how you are training your network?
Are you doing self-play games? Do you have access to a large number of GPUs?
2018-02-28 13:04 GMT-06:00 David Wu :
> It's not even just liberties and semeai, it's also eyes. Consider for
> example
Hoping to contact whoever is running these bots.
https://www.reddit.com/r/cbaduk/comments/7nacja/l011_bots_on_cgos_are_configured_wrong/
Copy/paste of my post on reddit:
Who is running the L011 bots on CGOS? I'm assuming they are Leela v0.11.
They are not configured to remove all dead stones
Is there some particular reason AGZ uses two 1x1 filters for the policy
head instead of one?
They could also have allowed more, but I guess that would be expensive? I
calculate that the fully connected layer has 2*361*362 weights, where 2 is
the number of filters.
By comparison the value head
How do you interpret this quote from the AGZ paper?
"Surprisingly, shicho (“ladder” capture sequences that may span the whole
board) – one of the first elements of Go knowledge learned by humans – were
only understood by AlphaGo Zero much later in training."
To me "understood" means the neural
Google has already announced their next step -- Starcraft2. But so far the
results they published aren't mind blowing like these.
2017-12-19 9:15 GMT-06:00 Fidel Santiago :
> Hello,
>
> I was thinking about this development and what it may mean from the point
> of view of a
I'm trying to implement the kgs-chat command but I'm getting a crash in
kgsGtp-3.5.22. Everything works fine and I can chat with the bot. But when
a match starts that's when it crashes. Here is my gtp log. Any ideas? Or
can someone paste me an example log that works or point me to example code?
Thanks for letting us know the situation Aja. It must be hard for an
engineer to not be able to discuss the details of his work!
As for the first-play-urgency value, if we indulge in some reading between
the lines: It's possible to interpret the paper as saying
first-play-urgency is zero. After
bility distribution from the
> policy output of the network, and over time it converges to using primarily
> the moves that have best results. So the details of how Q is initialized
> are not very relevant.
>
>
> On Sun, Dec 3, 2017 at 5:11 PM, Andy <andy.olsen...@gmail.com>
I am changing other variables at the same time.
- Andy
2017-12-03 14:30 GMT-06:00 Álvaro Begué <alvaro.be...@gmail.com>:
> The text in the appendix has the answer, in a paragraph titled "Expand and
> evaluate (Fig. 2b)":
> "[...] The leaf node is expanded and and
ding of the paper, please point to the
> specific paragraph that you are having trouble with.
>
> Álvaro.
>
>
>
> On Sun, Dec 3, 2017 at 9:53 AM, Andy <andy.olsen...@gmail.com> wrote:
>
>> I don't see the AGZ paper explain what the mean action-value Q(s,a)
>&g
I don't see the AGZ paper explain what the mean action-value Q(s,a) should
be for a node that hasn't been expanded yet. The equation for Q(s,a) has
the term 1/N(s,a) in it because it's supposed to average over N(s,a)
visits. But in this case N(s,a)=0 so that won't work.
Does anyone know how this
I agree with your main point that the first batch of games will be totally
random moves. I just wanted to make a small point that even for totally
random play, the network should be able to learn something about mid-game
positions as well. At move 100, a position with 50 white stones and 40
black
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.
2017-10-24 15:02 GMT-05:00 Gian-Carlo Pascutto
What is Ray? Strongest open source bot? Anyone have a link to it?
On Fri, Jan 6, 2017 at 3:39 AM, Hiroshi Yamashita wrote:
> If value net is the most important part for over pro level, the problem is
> making strong selfplay games.
>
> 1. make 30 million selfplay games.
> 2.
The Deepmind paper has a short section on the Rollout Policy they use, it
looks like they made some improvements on for their rollouts, maybe they
are better at handling semeai than previous methods. The response and
non-response patterns sound similar, but they also include liberty counts.
I
So the KGS bots darkforest and darkfores1 play with only DCNN, no MCTS
search added? I wish they would put darkfores2 with MCTS on KGS, why not
put your strongest bot out there?
2015-11-23 10:38 GMT-06:00 Petr Baudis :
> The numbers look pretty impressive! So this DNN is as
As of about an hour ago darkforest and darkfores1 have started playing
rated games on KGS!
2015-11-23 11:28 GMT-06:00 Andy <andy.olsen...@gmail.com>:
> So the KGS bots darkforest and darkfores1 play with only DCNN, no MCTS
> search added? I wish they would put darkfores2 with MCTS
Here is a simple working implementation.
https://github.com/pasky/michi
From the beginning of the readme:
Michi --- Minimalistic Go MCTS Engine
Michi aims to be a minimalistic but full-fledged Computer Go program based
on state-of-art methods (Monte Carlo Tree Search) and written in Python.
Our
Put it on cgos and see how good it is!
On Fri, Jul 10, 2009 at 9:10 PM, Michael Williams
michaelwilliam...@gmail.com wrote:
Now that I have this system of generating really big game trees, what sort
of interesting things could I do with it? The exact number of nodes I can
store is not exact
Yamato,
It looks like Zen19 doesn't implement handicap stone komi compensation
the same way kgs does for Chinese rules. It's the only reason I can
think that it lost this game:
http://files.gokgs.com/games/2009/3/27/TaPaHka-Zen19.sgf
- Andy, aka yoyoma
On Thu, Mar 26, 2009 at 5:54 PM, Yamato
FYI, here is a link and the relevant quotes for the way KGS gives
white extra komi based on handicap stones in some rules.
http://www.gokgs.com/help/rulesets.html
Chinese - The white player is given one point extra komi for every
handicap stone that Black gets at the start of the game.
AGA -
On Mon, Feb 16, 2009 at 7:45 PM, Andy andy.olsen...@gmail.com wrote:
See attached a copy of the .sgf. It was played private on KGS so you
can't get it there directly. One of the admins cloned it and I saved
it off locally.
I changed the result to be B+4.5 instead of W+2.5.
I forgot to make
I'm excited to see a computer reach 1d as well. For me I'm waiting to see a
bot hold a 1d rating consistently on kgs. Right now CrazyStone has been
rated 1d briefly, but hasn't been able to maintain it. It's currently 1k.
I put a small table of the progress of a few bot's ratings on kgs at
On Thu, Sep 4, 2008 at 11:09 AM, Rémi Coulom [EMAIL PROTECTED]wrote:
Andy wrote:
I'm excited to see a computer reach 1d as well. For me I'm waiting to see
a bot hold a 1d rating consistently on kgs. Right now CrazyStone has been
rated 1d briefly, but hasn't been able to maintain it. It's
, and accommodating this doesn't seem
like a big deal. Even on CGOS there is a 1s Bronstein delay to prevent
silly time loses due to lag. We need the same thing for humans except that
for humans it needs to be a bit more than 1s.
Same thing for bots on KGS.
- Andy
On Sun, Aug 10, 2008 at 3:46 PM, Robert Waite [EMAIL PROTECTED]wrote:
Okay.. so where is the paper that correlates the speed at which MCwUCT
approaches perfect play with the ability to play a human? They seem
unrelated as of yet.
The closest I've seen are these two studies Don made:
Remi, you mentioned how the other algorithms predicted well and guessed that
it's because the great majority of games are between experienced players
whose strength is not changing much. I also feel that the existing KGS
ratings work well for those players already. So how about focusing on how
But the program isn't stronger than pros, so how can it give better
information about proper komi?
On Feb 11, 2008 6:09 PM, Christoph Birk [EMAIL PROTECTED] wrote:
On Mon, 11 Feb 2008, Don Dailey wrote:
I don't bet, but if I did, I would bet that it's 7 or 8, and I'm
fairly certain that
See below I created a table that shows the transformation from KGS ratings
to the Elo that CGOS uses. I set 6k=1800 because I believe that is what GNU
3.7.10 is under both systems. Does anyone have more data points for bots
that play on both systems?
Also is there an all times list for 19x19?
There were some questions about the effective ELO difference of two players
3 ranks apart. Here are some links to information about go rating formulas,
and some statistics:
http://senseis.xmp.net/?KGSRatingMath
letters for
equivalent constants. So this varying of k is what accounts for the fact
that upsets are more likely for weak kyu players than for dan players.
- Andy
On Jan 31, 2008 12:37 PM, Don Dailey [EMAIL PROTECTED] wrote:
ELO ratings don't have to be absolute, just self consistent. So if you
CrazyStone hasn't played since the initial spike to 1k in December. The
movement of the chart afterwards is rating drift.
On Jan 31, 2008 12:49 PM, Gian-Carlo Pascutto [EMAIL PROTECTED] wrote:
Don Dailey wrote:
I don't know how David figures 1000 ELO, but I would expect the
difference to
is improving quickly so there is a strong upward drift. It's
usually not so bad for the strong kyus or dans since those players don't
generally improve very fast. However as I said in this case CS has only
played 10 games, so a much smaller pool of opponents is involved.
- Andy
On Jan 31, 2008 12
mcintyre [EMAIL PROTECTED] wrote:
From: Andy [EMAIL PROTECTED]
Sorry, the KGS formula uses a constant k which is different from the
K-factor in Elo.
P(A wins) = 1 / ( 1 + exp(k*(RankB-RankA)) )
This would be equivalent to changing the constant 400 in:
P(A wins) = 1 / ( 1 + 10^((Ra-Rb)/400
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