I'm still experimenting with it, so I cannot yet report any success. Nevertheless, feel free to experiment with txt and if you find it useful, please consider contributing.
Regards,
Years ago, I tried to do (almost) exactly what you describe there with Stockfish.
Even with a simple hill climb algorithm, the algorithm converged extremely fast into certain values.
Unfortunately the results were always a disaster in practical tests...
I think Komodo team has reported similar results.
But keep on trying. As always, the devil is in the details. So maybe someone can succeed in where I failed terribly.
zamar wrote:
Years ago, I tried to do (almost) exactly what you describe there with Stockfish.
Even with a simple hill climb algorithm, the algorithm converged extremely fast into certain values.
Unfortunately the results were always a disaster in practical tests...
I think Komodo team has reported similar results.
But keep on trying. As always, the devil is in the details. So maybe someone can succeed in where I failed terribly.
Some questions if you remember:
1) How many positions did you use?
2) How did you select those positions?
3) What did you try to tune?
4) Did you select only a small variables, or tuned them all?
zamar wrote:
Years ago, I tried to do (almost) exactly what you describe there with Stockfish.
Even with a simple hill climb algorithm, the algorithm converged extremely fast into certain values.
Unfortunately the results were always a disaster in practical tests...
I think Komodo team has reported similar results.
But keep on trying. As always, the devil is in the details. So maybe someone can succeed in where I failed terribly.
Some questions if you remember:
1) How many positions did you use?
2) How did you select those positions?
3) What did you try to tune?
4) Did you select only a small variables, or tuned them all?
1) Can't remember exactly, but I think there were at least several hundred thousands, maybe a million or so.
2) I selected quiet positions from CCRL game database. My definition for quiet was: eval = qsearch(), also excluded positions in the very late endgame.
3) Material (first order, second order), mobility
4) I think I did a few runs for different combinations.
zamar wrote:
2) I selected quiet positions from CCRL game database. My definition for quiet was: eval = qsearch(), also excluded positions in the very late endgame.
I also made another attempt where I modified qsearch() to actually save fens of all quiet positions it encountered during the search with certain low probability. After running a few thousand games, I had a collection of several millions positions.
Then I tried to use these games/positions, but again it was of no good...