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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #21 Posted: Sun Mar 03, 2019 9:12 am 
Honinbo

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According to AlphaGo Teach, there is a potentially serious human blind spot in a later position in this joseki. That is, humans prefer, by a ratio of 5 to 1 (49 to 10 by Waltheri's database), a play that is likely to be a mistake.

Click Here To Show Diagram Code
[go]$$Bcm11 Obvious wedge
$$ ---------------------------------------
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . O . . . . . , . . . . . X . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . , . . . . . , . . . . . , 8 . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . O . . . . . . . . . . . . . . . |
$$ | . . . . . X . 1 . . . . 7 . . . . . . |
$$ | . . . O . . . O X 3 5 . . . . X . . . |
$$ | . . . . . X . O 2 4 . 6 . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ ---------------------------------------[/go]


Given Black's thickness on the bottom side, this wedge, :w18:, has to be a good play, right? Lee Changho played it agains Cho Hunhyun in 1996. Well, AlphaGo doesn't like it. It gives Black an estimated win rate of 44.6%, 3.2% higher (worse for White) than its chosen play. That difference suggests that :w18: is a mistake.

What's the problem with it?

Click Here To Show Diagram Code
[go]$$Bcm19 Attack
$$ ---------------------------------------
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . O . . . . . , . . . . . X . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . , . . . . . , . . . . . , O . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . 3 . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . O . . . . . . . . . . . . . . . |
$$ | . . . . . X 1 X . . . . X . . . . . . |
$$ | . . . O . . . O X X X . . . . X . . . |
$$ | . . . . . X . O O O . O . 2 . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ ---------------------------------------[/go]


Cho Hunhyun continued with a sequence AlphaGo likes. He connected at :b19: with sente and then :b21: attacked White in the bottom left.

What should White have played instead of the wedge?

Click Here To Show Diagram Code
[go]$$Wcm18 Connect
$$ ---------------------------------------
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . d b . . |
$$ | . . . O . . . . . , . . . . . X c . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . , . . . . . , . . . . . a 6 . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . O . . 2 4 . . . . . . . . . . . |
$$ | . . . . . X 1 X . . . . X . . . . . . |
$$ | . . . O . 5 3 O X X X . . . . X . . . |
$$ | . . . . . X . O O O . O . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ ---------------------------------------[/go]


:w18: - :w22: connects White's groups, obviously forestalling an attack against either one. Then :b23: takes a big point on the right side. This actually is in line with the go proverb to make urgent plays before big plays, but humans haven't particularly seen it that way. In Waltheri's database only two humans, Kato Masao and Zhang Wengdong picked AlphaGo's play. Then their opponents played at "a", but that's another question.

BTW, how does AlphaGo Teach continue from here, to deal with Black's imposing moyo? With the 3-3 invasion of at "b", of course. :o But doesn't that allow Black to build up his moyo with a block at "c"? Maybe so, but AlphaGo blocks at "d". :shock: Like I said, I don't understand this game. :lol:

BTW, AlphaGo doesn't particularly like :b17:, the 5th line keima, giving it a winrate of only 41.5%. Well, that kind of makes sense, if the moyo isn't that big a deal. What does AlphaGo like? Why, the 3-3 invasion in the top left corner, of course! ;-) It gives it a winrate of 44.0%, 2.5% better than the keima. :b17: is another potential human blind spot, chosen by humans more than 95% in Waltheri's database. Needless to say, humans chose the 3-3 invasion 0% of the time. That will change. ;)

Edit: And if the moyo isn't that big a deal, so that the keima is at least questionable, then maybe playing the press to build thickness isn't that big a deal, either. So maybe the pincer is better, eh?

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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #22 Posted: Sun Mar 03, 2019 9:51 am 
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With a recent LZ network, LZ wants to play the same way (as white) and also wish to block at d with black. If black block the "old way", it the continuation looks like this :

Click Here To Show Diagram Code
[go]$$Wcm
$$ ---------------------------------------
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . 5 . . . . . |
$$ | . . . . . . . . . . . . 7 . . 3 1 . . |
$$ | . . . O . . . . . , . . . . 4 X 2 . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . , . . . . . , . . . 6 . . X . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . O . . X X . . . . . . . . . . . |
$$ | . . . . . X O X . . . . X . . . . . . |
$$ | . . . O . O O O X X X . . . . X . . . |
$$ | . . . . . X . O O O . O . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ ---------------------------------------[/go]


And LZ think white is fine, with 61.5% winrate.

And white like the R4 (or R9) invasion a couple moves later (when it get sente back)


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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #23 Posted: Sun Mar 03, 2019 10:48 am 
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Bill Spight wrote:
I wonder if both human and bot preferences are the result of path dependency, of different sorts: historical for humans, computational for bots. IIRC, in chess Emmanuel Lasker said if you find a good move, look for a better one. MCTS bots, it seems, don't do that so much.


That is a fascinating question! In particular, I wonder how many unexplored things get left behind in the process of a Zero-bot training - it seems obvious that, while spending limited amount of computational resources during training, there will be moves that are only "superficially" analyzed, then the bot is building some "misconception" (relatively speaking) which translates later in not considering enough some moves that can turn out to be better (if judged by the full game tree).

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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #24 Posted: Sun Mar 03, 2019 10:55 am 
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MikeKyle wrote:
Thanks Sorin, Bill Spight for your thoughts.

sorin wrote:
..
AlphaZero gives "b" 2% more compared to "a"
..

Are you referring to the original alphago teaching tool? I was under the impression that the teaching tool was based on AlphaGo Master ie. somewhere around the version that beat Ke Jie.


Yes, this tool: https://alphagoteach.deepmind.com/
You must be right, given the time when this was released, I guess it is based on the version of AlphaGo that had some influence from human games.

Which means to me that the places in the opening where it differs fundamentally from human play are even more interesting, and those will be even more differentiated in the AlphaZero version.

MikeKyle wrote:
I'd love to be proven wrong, but I didn't think that we had any resources based on AlphaGo Zero or AlphaZero except for the bot vs bot games that they published? (and of course the papers, leading to all these brilliant bots we now have!)


Oh, how I would love to prove you wrong! :-)
On the other hand, it matters less and less - since we have access to open source bots which eventually will reach (and surpass) the published AlphaGo version.

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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #25 Posted: Sun Mar 03, 2019 11:04 am 
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Bill Spight wrote:
According to AlphaGo Teach, there is a potentially serious human blind spot in a later position in this joseki. That is, humans prefer, by a ratio of 5 to 1 (49 to 10 by Waltheri's database), a play that is likely to be a mistake.

[...]

What should White have played instead of the wedge?

[...]

BTW, how does AlphaGo Teach continue from here, to deal with Black's imposing moyo?



AlphaGo proverb #1: "Wedge is not an option."

AlphaGo proverb #2: "There is no such thing as an 'imposing moyo'."

Sorry, I couldn't resist the temptation to anthropomorphise AlphaGo :-)

(EDIT: maybe it is really just proverb #0: "Because there is no such thing as an 'imposing moyo', wedge is not an option" :-) )

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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #26 Posted: Sun Mar 03, 2019 2:51 pm 
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Bill Spight wrote:
IIRC, in chess Emmanuel Lasker said if you find a good move, look for a better one.

Indeed! Don't forget dfan's corollary: "If you find a bad move, look for a better one too."

Quote:
MCTS bots, it seems, don't do that so much.

Of course it is an open question whether they do this as much as they should, but this question (called "exploration vs exploitation") is definitely one of the dominant issues for anyone studying reinforcement learning (in which agents choose actions and learn from experience), including the DeepMind folks. It is really unclear how you should balance the two behaviors! Alpha Zero and its progeny use an approach that tries to maximize effort towards lines with the greatest "upper confidence bound": that is, the moves that seem to have the maximum plausible upside. As it explores one good move, the maximum plausible upside of it will often decrease (because it learns more about it, so the uncertainty of its evaluation, both positive and negative, goes down), which causes it to then devote more energy to other moves that still have some unexplored promise.

Of course you can play with these parameters and algorithms all you like, and people have, but it's difficult. There are many instances on the Leela Zero project page of the following play in three acts:
  • Someone notices that Leela Zero got too excited about a suboptimal move in a certain position and didn't sufficiently explore another promising one.
  • They suggest a modification to Leela Zero's parameters to make it explore more in certain circumstances, with the result that Leela Zero now correctly finds the optimal move in that test position.
  • The change turns out to make Leela Zero weaker overall.

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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #27 Posted: Sun Mar 03, 2019 5:18 pm 
Honinbo

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dfan wrote:
Bill Spight wrote:
IIRC, in chess Emmanuel Lasker said if you find a good move, look for a better one.

Indeed! Don't forget dfan's corollary: "If you find a bad move, look for a better one too."


;) :D

Quote:
MCTS bots, it seems, don't do that so much.

Quote:
Of course it is an open question whether they do this as much as they should, but this question (called "exploration vs exploitation") is definitely one of the dominant issues for anyone studying reinforcement learning (in which agents choose actions and learn from experience), including the DeepMind folks. It is really unclear how you should balance the two behaviors! Alpha Zero and its progeny use an approach that tries to maximize effort towards lines with the greatest "upper confidence bound": that is, the moves that seem to have the maximum plausible upside. As it explores one good move, the maximum plausible upside of it will often decrease (because it learns more about it, so the uncertainty of its evaluation, both positive and negative, goes down), which causes it to then devote more energy to other moves that still have some unexplored promise.

Of course you can play with these parameters and algorithms all you like, and people have, but it's difficult. There are many instances on the Leela Zero project page of the following play in three acts:
  • Someone notices that Leela Zero got too excited about a suboptimal move in a certain position and didn't sufficiently explore another promising one.
  • They suggest a modification to Leela Zero's parameters to make it explore more in certain circumstances, with the result that Leela Zero now correctly finds the optimal move in that test position.
  • The change turns out to make Leela Zero weaker overall.


Thanks. I was responding to the remarkable imbalance in this case of the number of visits per candidate move by Leela Zero. In first position shown by sorin, the pincer at 43.3% winrate gets 13K visits, while the press at 43.0% gets 43 visits. In addition there are 7 more candidate moves, the worst winrate among them being 42.2%, and the most visits being 811. In terms of the goal of winning the game, it is hard to tell which of those 9 moves is better, but the bots consistently spend much more time considering the pincer and end up choosing it. That suggests a path dependency, but bots with different histories do the same thing. :scratch: OC, all a bot has to do is choose a good enough move. :)

In the second position there are also nine candidates shown. The pincer has the highest winrate of 44.2% and the highest visit count of 12K. In this case the three candidates in the top left corner have decent visit counts of more than 1K, and two of the plays have win rates of 44.1%. Still, the smallest winrate is 43.0%. Any of those candidates could be best. But the bots like the pincer. ;)

I did not mean to suggest that the bots did not make the best choices to play well. That is a different question. But, as you know, my advice to humans who are learning from bots is don't strain after gnats. OC, it would help to know the margin of error of a bot's winrate estimates, but nobody knows that. It is quite clear that it is at least 2% for AlphaGo.

----

On the question of missing the right play, in playing around with Deep Leela I have discovered that yes, just as it seemed, Leela 11 makes mistakes in the endgame. This afternoon I was going over a pro game where White resigned when DL said that its winrate was hovering around 30%. Just to see how the winrate changed as the end of the game approached, I let Deep Leela play on. White actually won the game when Black let White make a Bent Four in the Corner. :lol: Actually, for some time Black could have thrown in to make a ko, but eventually White made a bent four.

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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #28 Posted: Sun Mar 03, 2019 6:11 pm 
Judan

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Regarding the 2-space jump in the corner b after the pincer and jump, I can't claim to understand it but I have played around with it a bit in Lizzie and tried it in a few games. Here are some possible ways it can be a pro rather than a con.

Click Here To Show Diagram Code
[go]$$W
$$ --------------------+
$$ . . . . . . . . . . |
$$ . . . . . . . . . . |
$$ . . . . . . . . . . |
$$ , . . b a . X . . . |
$$ . . . . . . . . . . |
$$ . . . . . 1 . O . . |
$$ . . . . . . . . . . |
$$ . . . . . . . X . . |
$$ . . . . . . . . . . |
$$ . . . . . . , . . . |
$$ . . . . . . . . . . |[/go]


1) It's easy to tenuki white's next one point jump at 5 (which is quite likely to happen in a natural flow if you counter pincer with 3 because it didn't get ahead with the happy followup at a you would have with 2 one line to the right. (But actually even that followup is not as good as I thought is a general lesson from bots, ie jump to 5 is not clear sente even with black one-point jump).

Click Here To Show Diagram Code
[go]$$W
$$ --------------------+
$$ . . . . . . . . . . |
$$ . . . . . . . . . . |
$$ . . . . . . . . . . |
$$ , . a 2 . . X . . . |
$$ . . . . . . . . . . |
$$ . . . 5 . 1 . O . . |
$$ . . . . . . . . . . |
$$ . . . . . 4 . X . . |
$$ . . . . . . . . . . |
$$ . . . . . . , 3 . . |
$$ . . . . . . . . . . |[/go]


2) If at a later point white ends up playing 3-3 and you want to block the top side it's more efficient to be further away. Obviously 4 will often prefer to separate at a if there is a good attack on the outside stones, but even then white can get a fairly comfortable corner life (even with the high one space jump) so you give up a lot of cash for that speculative profit, and if the outside is already settled you probably prefer this direction switch.
Click Here To Show Diagram Code
[go]$$W
$$ --------------------+
$$ . . . . . . . . . . |
$$ . . . . . . . 6 5 . |
$$ . . . . . . 4 1 7 . |
$$ , . . X . . X 2 3 . |
$$ . . . . . . . . a . |
$$ . . . . . O . O . . |
$$ . . . . . . . . . . |
$$ . . . . . . . X . . |
$$ . . . . . . . . . . |
$$ . . . . . . , . . . |
$$ . . . . . . . . . . |[/go]


3) Black closing the corner with iron pillar later is more efficient and therefore more worthwhile to spend a move on, bigger (not-quite) territory. Iron pillar also threatens the 2nd line connection to the pincer stone so is a nice way to take profit, probing opponent do they want to play a less valuable move to prevent that emergency connection in gote. Here's a half board example (5 solid connect rather than extend joseki is also interesting to avoid giving momentum to settle):
Click Here To Show Diagram Code
[go]$$B
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . O . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . X . 3 5 . . . . . . . . . . |
$$ | . . . O . . . 2 1 , . . . . . X . . . |
$$ | . . . 8 . X . O 4 . . 6 . 7 . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ +---------------------------------------+[/go]


Of course there are also cons, the most obvious being the thinness of 2-space jump to various attaches and cuts. LZ is sometimes willing to allow them to be split if you get a solid chunky corner territory out of it (e.g. from a ponnuki).


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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #29 Posted: Sun Mar 03, 2019 6:32 pm 
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One thing that bothers me about the highly unbalanced comparison between plays is that, when the winrate estimates are close between two plays, shouldn't the number of visits for second place be roughly the same as the number of visits for the eventual winner of the comparison? That is, in general the more visits a play gets, the smaller the margin of error of its evaluation should be. So it seems that we have a case where the margins of error of two competing evaluations overlap, and instead of reducing the larger margin of error, much more effort is spent reducing the smaller margin of error. You could get it down to a point and there would still be overlap.

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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #30 Posted: Sun Mar 03, 2019 8:37 pm 
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Bill Spight wrote:
One thing that bothers me about the highly unbalanced comparison between plays is that, when the winrate estimates are close between two plays, shouldn't the number of visits for second place be roughly the same as the number of visits for the eventual winner of the comparison? That is, in general the more visits a play gets, the smaller the margin of error of its evaluation should be. So it seems that we have a case where the margins of error of two competing evaluations overlap, and instead of reducing the larger margin of error, much more effort is spent reducing the smaller margin of error. You could get it down to a point and there would still be overlap.


The relative difference in number of visits may be actually "noise" since the total number of visits is still quite small in my analysis earlier in this thread. Maybe someone with a stronger computer or more time can let LeelaZero do a deeper analysis and, with many more total visits, the pattern that you expect may be true, as in relatively close winrate moves would also have relatively close number of visits...

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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #31 Posted: Sun Mar 03, 2019 10:34 pm 
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sorin wrote:
Bill Spight wrote:
One thing that bothers me about the highly unbalanced comparison between plays is that, when the winrate estimates are close between two plays, shouldn't the number of visits for second place be roughly the same as the number of visits for the eventual winner of the comparison? That is, in general the more visits a play gets, the smaller the margin of error of its evaluation should be. So it seems that we have a case where the margins of error of two competing evaluations overlap, and instead of reducing the larger margin of error, much more effort is spent reducing the smaller margin of error. You could get it down to a point and there would still be overlap.


The relative difference in number of visits may be actually "noise" since the total number of visits is still quite small in my analysis earlier in this thread. Maybe someone with a stronger computer or more time can let LeelaZero do a deeper analysis and, with many more total visits, the pattern that you expect may be true, as in relatively close winrate moves would also have relatively close number of visits...


I volunteered to try what I suggested earlier, and I let the best 15-blocks LZ network run until it reached about 1.4M total visits.
I was surprised to see that around the 500K total visits point, the traditional human move moved up from #2 to #1, and it kept the place. Yay humans!! :-)

Also, as the number of visits increases, more and more weird moves are being explored, so basically any possible move has some non-zero probability of eventually being explored by MCTS.

It does seem to follow Bill's intuition, namely moves that are close winrate-wise get more attention and eventually get more visits; on the other hand, since new candidate moves pop up on the radar all the time, there will always be gaps, until a really huge number of visits happened (which I cannot experiment with on my laptop).
For instance, the low approach in the lower right corner is in the same winrate ballparck as top 2 moves on the left; it has way more visits than all other lower winrate candidates, but still way fewer than top two; I expect that with more visits, this 3rd candidate would also get more attention from MCTS.


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 Post subject: Re: MikeKyle analyses Hoshi, low approach, low 1 space pince
Post #32 Posted: Mon Mar 04, 2019 3:15 am 
Judan

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OGS: Uberdude 7d
sorin, very interesting about the press overtaking counter-pincer at 500k, I have little experience of such high visits. Makes one wonder how many of these other characteristic bot moves are just the dumb low visit intuition of the neural networks and with more time they would find the more subtle and refined human move :) .

Bill, about close winrates with low visits, that's why for reviewing I recommend playing that other move and letting it accumulate lots of playouts to get a winrate of similar accuracy. When playing as LeelaZero and manually doing this promising blue circle investigation to accelerate devotion of playouts to possibly better other moves I think I make it stronger, though as dfan says when programmatic changes have been made to switch the balance of exploration they've tended to make it weaker overall. Probably my strategy in when to do this is not as simplistic as fiddling a few MCTS hyper-parameters and might use a little of my Go skill. Also worth pointing out that all the moves in the AlphaGo teaching tool have 10 million playouts.

As for when you've found a good move, look for a better one, here's a very interesting one from the AlphaGo teaching tool Sinan Djepov pointed out. The knight move of 4 is a bit unusual compared to attach but human have played it, and AG and LZ and Elf all quickly prefer it. But the nozomi/probe/!! of 5 is an amazing idea. I didn't wait for a million playouts to see if LZ finds it itself, but it like it when you show it. AG recommends tenuki (tohugh they don't tell us if answer is bad and if so how much) but exploring with LZ shows how it can be a nice reduction kikashi before taking the corner if white makes normal h5 answer, and k4 is another nice followup if g4.

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This post by Uberdude was liked by 2 people: Bill Spight, sorin
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