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

Gian-Carlo Pascutto gcp at sjeng.org
Thu Dec 7 05:17:07 PST 2017

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?


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