Hi.
First things first : i think the specification is enougth as it is.
I hope that we can end up with something useable by anyone,
even non go people as a final goal. We'll have to get non-go experienced
people to beta-read it (and criticized) for us, i suppose. And we'll probably
have to get a HTML version with some fancy illustration (wich i won't be
helpfull for)
I really look forward to be able to get involved non go people easily :)
I'm pretty sure a lot of them accepting this contest would end up
being very valuable for the community :)
We'll probably have to get a bit deeper in the gtp-part ultimately.
--------------------------------------------------------------------------
===
5. Scoring is Chinese scoring. When a play-out completes, the
score is taken accounting for komi and statistics are kept.
I think i would like it if we just gave how it should be done.
Using the eye definition we impose anyway.
-------------
I propose :
-------------
5. Scoring is done at the end of a game (two consecutive pass)
, in the following way :
each stone on the board gives a point to the player who
owns it. An empty intersection gives a point to the player (if any) who
has a stone on each orthogonal intersection around it.
If black's score is greater than 0.0 then it is scored as a black win.
Otherwise
it is scored as a white win.
===
1. Must be able to play complete games for comprehensive conformity
testing.
I do not quite understand the point. But it can't really hurt either .. :)
===
2. In the play-out phase, the moves must be chosen in a "uniformly
random" way between legal moves that do not fill 1 point eyes and
obey the simple-ko restriction.
When a move in the play-out is not possible, a pass is given.
I'd like that we got more descriptive on the simple-ko restriction, if possible.
(i'll try to propose something, but i'm getting low on time right now)
===
3. Play-outs stop after 2 consecutive pass moves, OR when N*N*3
moves have been completed, except that at least 1 move gets tried
where N is the size of the board. So if the board is 9x9 then
the game is stopped after 9*9*3 = 81*3 = 243 move assuming at
least one move has been tried in the play-outs.
I don't quite get the point of the "except that at least 1 move gets tried"
part
===
ref-nodes -> return total moves executed in play-outs
(including both pass moves at end of each
play-out.)
ref-score -> return total win fraction for black.
i do not find ref-nodes that much descriptive for "return total moves executed
in play outs"
Maybe it is quite standard to call that number ref-nodes ? As it's only amaf,
there are no node per-se are there. what about a ref-numberOfMove command ?
--------------------------------------------------------------------------
Here are the data you requested for with the implementation i want
to use as a reference. I'm not able to get value for integer komi.
(my system do no account for draws ..)
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
Komi 0.5
mean score =0.5244261847821416
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
komi 5.5
mean score =0.44674397181685754
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
Komi 6.5
mean score =0.4467712426921182
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
Komi 7.5
mean score =0.42132957622630574
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
87317.15777498983 Playout/sec
Time=11.461321297
Number of playout=1000770.0
Mean moves per sim 111.06128680915695
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
It uses the mersene twister for random-generation.
But this is a 4 thread test. I use nanotime as an help
to set up the (per thread) seed, combined with thread number.
I think it's interesting to give the Playout/sec score.
Then your reference bot "can" be used as a refence benchmark.
That is not perfect of course, but that gives something to chew.
(I get quite a large variation in speed from run to run
with 1000 000 simulations. Ranging from less than 80k/s to close to 90k/s
with 4 threads over my 4 cores.
My implementation is in java too, and has nothing fancy to it,
so i might as well publish it later on. I probably should clean it
up a bit. And make a few optimisation (by refactoring).
----------------------------------
Don said :
----------------------------------
I made a reference bot and I want someone(s) to help me check it out
with equivalent data from their own program. There are no guarantees
that I have this correct of course.
Doing 1 million play-outs from the opening position I get the following
numbers for various komi:
playouts: 1,000,000
komi: 5.5
moves: 111,030,705
score: 0.445677
playouts: 1,000,000
komi: 6.0
moves: 111,066,273
score: 0.446729
playouts: 1,000,000
komi: 6.5
moves: 111,040,546
score: 0.447138
playouts: 1,000,000
komi: 7.0
moves: 111,029,204
score: 0.4333795
playouts: 1,000,000
komi: 7.5
moves: 111,047,843
score: 0.421281
(I also get a score of 0.524478 for 0.0 komi)
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