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

Marc Landgraf mahrgell87 at gmail.com
Mon May 22 12:01:45 PDT 2017


And talkig about tactical mistakes:
Another game, where a trick joseki early in the game (top right) completely
fools Leela. Leela here play this like it would be done in similar shapes,
but then gets completely blindsided. But to make things worse, it finds the
one way to make the loss the biggest. (note: this is not reliable when
trying this trick joseki, Leela will often lose the 4/5 stones on the left,
but will at least take the one stone on top in sente instead of screwing up
like it did here) Generally this "trick" is not that deep reading wise, but
given its similarity to more common shapes I can understand how the bot
falls for it.
Anyway, Leela manages to fully stabilize the game (given our general
difference in strength, this should come as no surprise), just to throw
away the center group.

But what you should really look at here is Leelas evaluation of the game.

Even very late in the game, the MC part of Leela considers Leela well
ahead, completely misreading the L+D here. Usually in most games Leela
loses to me, the issue comes the other way around. Leela NN strongly
believes in the game to be won, while the MC-part notices the real trouble.
But not here. Now of course this kind of misjudgement also could serve as
explaination how this group could die in first place.

But having had my own MC-Bot I really wonder how it could misevaluate so
badly here. To really lose this game as Black it either requires
substantial self ataris by Black, or large unanswered self atari by White.
Does Leela have such light playouts that those groups can really flip
status in 60%+ of the MC-Evaluations?

2017-05-22 20:46 GMT+02:00 Marc Landgraf <mahrgell87 at gmail.com>:

> Leela has surprisingly large tactical holes. Right now it is throwing a
> good number of games against me in completely won endgames by fumbling away
> entirely alive groups.
>
> As an example I attached one game of myself (3d), even vs Leela10 @7d. But
> this really isn't a onetime occurence.
>
> If you look around move 150, the game is completely over by human
> standards as well as Leelas evaluation (Leela will give itself >80% here)
> But then Leela starts doing weird things.
> 186 is a minor mistake, but itself does not yet throw the game. But it is
> the start of series of bad turns.
> 236 then is a non-threat in a Ko fight, and checking Leelas evaluation,
> Leela doesn't even consider the possibility of it being ignored. This is
> btw a common topic with Leela in ko fights - it does not look at all at
> what happens if the Ko threat is ignored.
> 238 follows up the "ko threat", but this move isn't doing anything either!
> So Leela passed twice now.
> Suddenly there is some Ko appearing at the top right.
> Leela plays this Ko fight in some suboptimal way, not fully utilizing
> local ko threats, but this is a concept rather difficult to grasp for AIs
> afaik.
> I can not 100% judge whether ignoring the black threat of 253 is correct
> for Leela, I have some doubts on this one too.
> With 253 ignored, the game is now heavily swinging, but to my judgement,
> playing the hane instead of 256 would still keep it rather close and I'm
> not 100% sure who would win it now. But Leela decides to completely bury
> itself here with 256, while giving itself still 70% to win.
> As slowly realization of the real game state kicks in, the rest of the
> game is then the usual MC-throw away style we have known for years.
>
> Still... in this game you can see how a series of massive tactical
> blunders leads to throwing a completely won game. And this is just one of
> many examples. And it can not be all pinned on Ko's. I have seen a fair
> number of games where Leela does similar mistakes without Ko involved, even
> though Ko's drastically increase Leelas fumble chance.
> At the same time, Leela is completely and utterly outplaying me on a
> strategical level and whenever it manages to not make screwups like the
> ones shown I stand no chance at all. Even 3 stones is a serious challenge
> for me then. But those mistakes are common enough to keep me around even.
>
> 2017-05-22 17:47 GMT+02:00 Erik van der Werf <erikvanderwerf at gmail.com>:
>
>> On Mon, May 22, 2017 at 3:56 PM, Gian-Carlo Pascutto <gcp at sjeng.org>
>> wrote:
>>
>>> On 22-05-17 11:27, Erik van der Werf wrote:
>>> > On Mon, May 22, 2017 at 10:08 AM, Gian-Carlo Pascutto <gcp at sjeng.org
>>> > <mailto:gcp at sjeng.org>> wrote:
>>> >
>>> >     ... This heavy pruning
>>> >     by the policy network OTOH seems to be an issue for me. My program
>>> has
>>> >     big tactical holes.
>>> >
>>> >
>>> > Do you do any hard pruning? My engines (Steenvreter,Magog) always had a
>>> > move predictor (a.k.a. policy net), but I never saw the need to do hard
>>> > pruning. Steenvreter uses the predictions to set priors, and it is very
>>> > selective, but with infinite simulations eventually all potentially
>>> > relevant moves will get sampled.
>>>
>>> With infinite simulations everything is easy :-)
>>>
>>> In practice moves with, say, a prior below 0.1% aren't going to get
>>> searched, and I still regularly see positions where they're the winning
>>> move, especially with tactics on the board.
>>>
>>> Enforcing the search to be wider without losing playing strength appears
>>> to be hard.
>>>
>>>
>> Well, I think that's fundamental; you can't be wide and deep at the same
>> time, but at least you can chose an algorithm that (eventually) explores
>> all directions.
>>
>> BTW I'm a bit surprised that you are still able to find 'big tactical
>> holes' with Leela now playing as 8d KGS
>>
>> Best,
>> Erik
>>
>>
>>
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>>
>
>
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