[Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Brian Sheppard sheppardco at aol.com
Thu Dec 7 07:54:45 PST 2017


> Which is IMHO missing the point a bit ;-)

I saw it the same way, while conceding that the facts are accurate.

It makes sense for SF to internalize the details before making decisions. At some point there will be a realization that AZ is a fundamental change.


>What about the data point that AlphaGo Zero gained 2100 Elo from its tree search? In a game commonly considered less tactical?

That is a common perception, especially among those who have never debugged a Go program. :-)

I was coming at it from the other direction, reasoning that since SF and AZ are close to perfect at chess, then there is less to gain from speed. (Whereas I doubt that AGZ is close to perfect at Go.)
	
All of this is subject to my money back guarantee: my opinions are guaranteed wrong, or your money back. :-)


-----Original Message-----
From: Computer-go [mailto:computer-go-bounces at computer-go.org] On Behalf Of Gian-Carlo Pascutto
Sent: Thursday, December 7, 2017 8:17 AM
To: computer-go at computer-go.org
Subject: Re: [Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

On 7/12/2017 13:20, Brian Sheppard via Computer-go wrote:
> The conversation on Stockfish's mailing list focused on how the match 
> was imbalanced.

Which is IMHO missing the point a bit ;-)

> My concern about many of these points of comparison is that they 
> presume how AZ scales. In the absence of data, I would guess that AZ 
> gains much less from hardware than SF. I am basing this guess on two 
> known facts. First is that AZ did not lose a game, so the upper bound 
> on its strength is perfection. Second, AZ is a knowledge intensive 
> program, so it is counting on judgement to a larger degree.

What about the data point that AlphaGo Zero gained 2100 Elo from its tree search? In a game commonly considered less tactical?

--
GCP
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