David Silver wrote:
>A: Estimate value V* of every position in a training set, using deep
>rollouts.
>
>B: Repeat, for each position in the training set
> 1. Run M simulations, estimate value of position (call this V)
> 2. Run another N simulations, average the value of psi(s,a) over
Hi Remi,
What komi did you use for 5x5 and 6x6 ?
I used 7.5 komi for both board sizes.
I find it strange that you get only 70 Elo points from supervised
learning over uniform random. Don't you have any feature for atari
extension ? This one alone should improve strength immensely (extend
stri
David Silver wrote:
Hi Michael,
But one thing confuses me: You are using the value from Fuego's 10k
simulations as an approximation of the actual value of the position.
But isn't the actual
value of the position either a win or a loss? On such small boards,
can't you assume that Fuego is ab
Hi Yamato,
Could you give us the source code which you used? Your algorithm is
too complicated, so it would be very helpful if possible.
Actually I think the source code would be much harder to understand!
It is written inside RLGO, and makes use of a substantial existing
framework that w
Donn, your email at d...@mit.edu is bouncing.
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Hi all,
I've been looking through the archives trying to find out if anybody had
published information on the following during uniform random playouts
and/or real games:
- Frequency of string merges
- Frequency of placing stones in empty space
- Average length of strings, etc.
I noticed th
