Re: [Computer-go] Significance of resignation in AGZ

[Brian Sheppard via Computer-go]

I didn’t measure precisely because as soon as I saw the training artifacts I 
changed the code. And I am not doing an AGZ-style experiment, so there are 
differences for sure. So I will give you a swag…

 

Speed difference is maybe 20%-ish for 9x9 games.

 

A frequentist approach will overstate the frequency of fill-in plays by a 
pretty large factor, because fill-in plays are guaranteed to occur in every 
game but are not best in the competitive part of the game. This will affect the 
speed of learning in the early going.

 

The network will use some fraction (almost certainly <= 20%) of its capacity to 
improve accuracy on positions that will not contribute to its ultimate 
strength. This applies to both ordering and evaluation aspects.

 

 

 

 

From: Andy [mailto:andy.olsen...@gmail.com] 
Sent: Friday, December 1, 2017 4:55 PM
To: Brian Sheppard ; computer-go 

Subject: Re: [Computer-go] Significance of resignation in AGZ

 

Brian, do you have any experiments showing what kind of impact it has? It 
sounds like you have tried both with and without your ad hoc first pass 
approach?

 

 

 

 

2017-12-01 15:29 GMT-06:00 Brian Sheppard via Computer-go 
 >:

I have concluded that AGZ's policy of resigning "lost" games early is somewhat 
significant. Not as significant as using residual networks, for sure, but you 
wouldn't want to go without these advantages.

The benefit cited in the paper is speed. Certainly a factor. I see two other 
advantages.

First is that training does not include the "fill in" portion of the game, 
where every move is low value. I see a specific effect on the move ordering 
system, since it is based on frequency. By eliminating training on fill-ins, 
the prioritization function will not be biased toward moves that are not 
relevant to strong play. (That is, there are a lot of fill-in moves, which are 
usually not best in the interesting portion of the game, but occur a lot if the 
game is played out to the end, and therefore the move prioritization system 
would predict them more often.) My ad hoc alternative is to not train on 
positions after the first pass in a game. (Note that this does not qualify as 
"zero knowledge", but that is OK with me since I am not trying to reproduce 
AGZ.)

Second is the positional evaluation is not training on situations where 
everything is decided, so less of the NN capacity is devoted to situations in 
which nothing can be gained.

As always, YMMV.

Best,
Brian


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[Computer-go] Significance of resignation in AGZ

[Brian Sheppard via Computer-go]

I have concluded that AGZ's policy of resigning "lost" games early is somewhat 
significant. Not as significant as using residual networks, for sure, but you 
wouldn't want to go without these advantages.

The benefit cited in the paper is speed. Certainly a factor. I see two other 
advantages.

First is that training does not include the "fill in" portion of the game, 
where every move is low value. I see a specific effect on the move ordering 
system, since it is based on frequency. By eliminating training on fill-ins, 
the prioritization function will not be biased toward moves that are not 
relevant to strong play. (That is, there are a lot of fill-in moves, which are 
usually not best in the interesting portion of the game, but occur a lot if the 
game is played out to the end, and therefore the move prioritization system 
would predict them more often.) My ad hoc alternative is to not train on 
positions after the first pass in a game. (Note that this does not qualify as 
"zero knowledge", but that is OK with me since I am not trying to reproduce 
AGZ.)

Second is the positional evaluation is not training on situations where 
everything is decided, so less of the NN capacity is devoted to situations in 
which nothing can be gained.

As always, YMMV.

Best,
Brian


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