-
Notifications
You must be signed in to change notification settings - Fork 0
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Model compare: figure out how to add noise while comparing #87
Comments
Maybe sample random openings from an opening book, which are still considered equal |
What do you think about the noisy-beginning idea? |
The noisy beginning will not result in an equalized position for the rest of the game, im not sure. |
I think it could be okay that the position is not equalized, because
(a) the moves are still chosen by the players (just with some added 'luck'),
(b) that's why we do many comparisons - there is some 'luck' involved
I think it's much more elegant if the whole training flow has no human
knowledge, or at least, no more human knowledge than in AlphaZero.
Or at least, we should have a *default* workflow as such. (and possibly
other ones too)
BTW you probably saw it, but I like this formulation in their paper
![image](https://user-images.githubusercontent.com/4618146/197798679-17019750-46c7-4ec3-9f07-3c6e616bf720.png)
|
Alright, i agree its more elegant without opening book, but im still think we should look for a better solution. |
Do you think we can rely here on the noise from the floating-point errors in the network activation? And multithreading |
fp no, multithreading yes, but we dont have multithreading in a single search, we have multithreading of multiple searchs, so currently it doesn't have any affect |
In the paper it say "t -> 0", so maybe they just use very small temperature, i think that reasonable |
No description provided.
The text was updated successfully, but these errors were encountered: