On 8/03/19 16:14, David Wu wrote:
> I suspect Leela Zero would come off as far *less* favorable if one
> tried to do such a comparison using their actual existing code rather
> than abstracting down to counting neural net evals, because as far as
> I know in Leela Zero there is no cross-game batchi
>> contrary to intuition built up from earlier-generation MCTS programs in Go,
>> putting significant weight on score maximization rather than only
>> win/loss seems to help.
This narrative glosses over important nuances.
Collectively we are trying to find the golden mean of cost efficiency...
On Fri, Mar 8, 2019 at 8:19 AM Darren Cook wrote:
> > Blog post:
> > https://blog.janestreet.com/accelerating-self-play-learning-in-go/
> > Paper: https://arxiv.org/abs/1902.10565
>
> I read the paper, and really enjoyed it: lots of different ideas being
> tried. I was especially satisfied to see
> Blog post:
> https://blog.janestreet.com/accelerating-self-play-learning-in-go/
> Paper: https://arxiv.org/abs/1902.10565
I read the paper, and really enjoyed it: lots of different ideas being
tried. I was especially satisfied to see figure 12 and the big
difference giving some go features made
From before AlphaGo was announced, I thought the way forward was
generating games that play to the bitter end maximizing score, and
then using the final ownership as something to predict. I am very glad
that someone has had the time to put this idea (and many others!) into
practice. Congratulations
