Quoting Darren Cook <[email protected]>:
* The scaling behavior might be different. E.g. if Fuego and Valkyria
are both run with 10 times more playouts the win rate might change. Just
to dismiss an algorithm that loses at time limits that happen to suit
rapid testing on today's hardware could mean we miss out on the ideal
algorithm for tomorrow's hardware. (*)
I just happened to have experimental data on exactly this topic. This
is Valkyria vs Fuego where I scale the number of playouts (Sims) x4 in
each row.
Sims Winrate Err N WR EloDiff
2 99.2 0.4 500 0.992 837
8 98.2 0.6 500 0.982 696
32 94.2 1 500 0.942 484
128 88.8 1.4 500 0.888 360
512 82 1.7 500 0.82 263
2048 83.2 1.7 499 0.832 278
8192 81.3 1.7 497 0.813 255
32768 75.5 3.6 139 0.755 196
The data shows clearly that the 0.3.2 version of Fuego I use, probably
plays really bad moves with a high frequency in the playouts. With
more playouts a lot of these blunders can be avoided I guess and the
win rate goes down from 99% towards 80%. The question here if it goes
asymptotically towards 80% or perhaps 50% with more simulations.
Unfortunately I cannot continue this plot because I run out of memory
and it takes ages to finish the games.
So the question is then: are there a fixed gain of the heavy playouts
with more than 512 simulations or does the effect of the heavy
playout get less and less important with larger tree size.
Note also that this also not only a matter of having heavy playouts or
not. There is also a difference in tree search since Valkyria and
Fuego probably search their trees differently, and it could be that
Valkyria search deep trees
inefficiently.
Maybe I should run a similar test against a light version of Valkyria
to control for the search algorithm.
-Magnus
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