I haven't thought clearly about the 7x7 case, but on 19x19 I think it would
suffer both challenges -- you'd count dead stuff as alive quite frequently,
and because you're pruning the game ending early you might be getting wrong
who has actually won. That's why some people use less ambiguous definit
Oh, I see. I believe I am, in fact, using Tromp-Taylor rules for scoring. I was
unaware that that's what it was called.
‐‐‐ Original Message ‐‐‐
On Sunday, December 9, 2018 10:09 PM, cody2007 wrote:
> Sorry, just to make sure I understand: your concern is the network may be
> learning
Sorry, just to make sure I understand: your concern is the network may be
learning from the scoring system rather than through the self-play? Or are you
concerned the scoring is giving sub-par evaluations of games?
The scoring I use is to simply count the number of stones each player has on
the
Imagine that your score estimator has a better idea about the outcome of
the game than the players themselves.
Then you can build a stronger computer player with the following algorithm:
use the score estimator to pick the next move after evaluating all legal
moves, by evaluating their after-move
>By the way, why only 40 moves? That seems like the wrong place to economize,
>but maybe on 7x7 it's fine?
I haven't implemented any resign mechanism, so felt it was a reasonable balance
to at least see where the players roughly stand. Although, I think I errored on
too few turns.
>A "scoring e
A "scoring estimate" by definition should be weaker than the computer
players it's evaluating until there are no more captures possible.
Yes?
s.
On Sun, Dec 9, 2018, 5:49 PM uurtamo By the way, why only 40 moves? That seems like the wrong place to
> economize, but maybe on 7x7 it's fine?
>
> s.
By the way, why only 40 moves? That seems like the wrong place to
economize, but maybe on 7x7 it's fine?
s.
On Sun, Dec 9, 2018, 5:23 PM cody2007 via Computer-go <
computer-go@computer-go.org wrote:
> Thanks for your comments.
>
> >looks you made it work on a 7x7 19x19 would probably give better
Thanks for the tutorial! I have some questions about training
a) Do you use Dirichlet noise during training, if so is it limited to first
30 or so plies ( which is the opening phase of chess) ?
The alphazero paper is not clear about it.
b) Do you need to shuffle batches if you are doing one epoch
Thanks for your comments.
>looks you made it work on a 7x7 19x19 would probably give better result
>especially against yourself if you are a complete novice
I'd expect that'd make me win even more against the algorithm since it would
explore a far smaller amount of the search space, right?
Certa
looks you made it work on a 7x7 19x19 would probably give better result
especially against yourself if you are a complete novice
for not cheating against gnugo, use --play-out-aftermath of gnugo parameter
If I don't mistake a competitive ai would need a lot more training such
what does leela zero
Hi all,
I've posted an implementation of the AlphaZero algorithm and brief tutorial.
The code runs on a single GPU. While performance is not that great, I suspect
its mostly been limited by hardware limitations (my training and evaluation has
been on a single Titan X). The network can beat GNU
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