I have already posted the following results. The results shows the
winrates of Valkyria 3.2.0 against gnugo at default strength.
512 Simulations per move
UCT_K Winrate SERR
0 58.8 2.1 (Winrate only)
0.01 56.8 2.2
0.1 60.9 2.2
0.5 54.2 2.2
1 50.6 2.2
With 512 simulations there is not much work done in the tree. So I
extend the test to 2048 simulations and also added the parameter value
2 to see what happens when search get really wide.
2048 Simulations per move
UCT_K Winrate SERR
0 80.7 2.3 (Winrate only)
0.01 83.3 2.2
0.1 83.7 2.1
0.5 77.3 2.4
1 71.3 3
2 62 4.9
The number of games are 300 for parameters 0 to 0.5 and a little less
for parameter values 1 and 2
The results confirm that Valkyria still benefits from using confidence
bounds with UCT, although the effect is really small.
Also the effect of the constant might be a little greater with 2048
simulations rather than for 512. Still the curves look more or less
the same. Does anyone have experience doing a test with different
amounts of simulations where the best parameter value depend on the
number of simulations?
I prefer to use a low amount of simulations since it is simply faster,
and also if the winrate of Valkyria gets to close to 100% it becomes
harder to measure the effect of different parameter settings. Maybe I
should quit testing against gnugo, and try something stronger.
-Magnus
_______________________________________________
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/
