And wrote:
frmor, thank you so much for this project!
I correctly understood, these recommendations for the case if SAI are used to play against another program (human)?
which values are better if the opponent is stronger? and mu = 0.5 or 1 can be used, and what will it give?
It's a bit complicated, and you should read the
paper to get full details. Let us say that lambda and mu control the two sides of the interval of komi values over which the winrate is averaged.
If mu=0 it means to keep the real komi as one side.
If lambda=1 it means to use, for the other side, the komi value that SAI thinks would make the current position fair (in the sense of 50% winrate).
Intermediate values are to be intended in the winrate scale, not komi scale, so that for example, if SAI thinks it is ahead, with winrate 80% and lambda=0.3, than it would compute the weighted mean 71% = 0.3 * 50% + 0.7 * 80%, look for the komi value that would give itself a 71% winrate, and then use this as the side.
So if SAI is black and real komi is 7.5, then this virtual komi value would be something more, say 9.3. Then SAI for the exploration of the current move would compute the nodes winrates as averages over the komi range [7.5 ; 9.3] and choose its move accordingly.
You can put mu=lambda>0 and in this way SAI would play as if the komi was the one computed, hence giving itself increasing score handicap when ahead. (And the opposite when behind.)
My recommendation is to use lambda=mu=0 when against a strong opponent, to try hard to win with the real komi.
You should use values larger than 0 when against a weaker opponent, both to improve the final score, when playing a fair game, and to improve the strength of play when starting with handicap.
When SAI is behind with a weaker opponent because of handicap, positive values of lambda (and maybe mu) were showed to improve its ability to recover from the initial disadvantage.