Forum Archive :
If you do a GNU rollout which terminates once a certain JSD is reached
then what is the likely 'actual' JSD when the rollout stops.
I say this as the JSD seems to be a bit of a 'random walk' thats
ultimately headed to zero (if the outcomes are identical) or infinty if
not. But if I stop the first time I get to say 3 jsds, am I not really
getting the full 3 jsds worth of 'confidence'.
I guess its going to depend somehow on the number of trials / volatility
of the position as well somehow?
Maik Stiebler writes:
A typical example of a rollout setting is: stop when plays are x JSDs
apart, but do at least 300 trials and not more than 10000.
I simulated this by drawing rollout results from a uniform distribution
(which is probably not a very good model). Instead of JSDs I used
(current deviation of the sample mean from the known population mean)
/(standard error as estimated from the rollout).
With x=2.5 and settings as above, the rollout hit the stopping criteria
before the 10000th trial (i.e., it gave a 'wrong' result) in 15% of the
cases, with x=2.7 it happened 10% of the time.
Then I changed the settings to at least 500 trials, and for x=2.5 the
number of wrong results decreased to 10%. Then I allowed the rollouts to
be extended to 30000 trials and, of course, it got worse again: The
probability of a wrong result was more than 40% for x=2.0, but less than
6% for x=3.0.
I'd conclude from that that a threshold of 3 JSDs is usually very good
in practice and that 2 JSDs may be a bit too low really to get good
I cannot see how that would depend on the volatility of the position. It
does depend on the number of trials in the sense that the lower the
minimum number of trials and the larger the maximum number, the worse
the confidence gets. If you decide before the rollout that you will do
exactly N trials, then x=2.0 will give you fine confidence as long as N
is sufficiently large (say larger than 100).
Adrian Wright writes:
Thanks, that's helpful. What I meant by the volatility of the position
was that some situations, such as a last roll position or a relatively
low contact position will converge very rapidly, whereas others my be
inherently much more complex with many possible bi-furcations, say a
prime vs prime or back game. I would have thought the more complex
positions would require more trials to get an equivalent confidence.
If I've understood you correctly then if I'm doing 2ply-2ply rollouts
and stopping when over 3 jsd with minimum 648 trials and maximum 3888
that should produce a high degree of confidence.
Maik Stiebler writes:
The simpler the position, the better variance reduction will work, i.e.
you get lower standard errors with fewer trials. That is very helpful
and will tend to reduce the number of trials needed to reach the 3 JSDs
difference threshold. It does not affect the relationship between # of
JSDs and confidence levels, however.
The settings you give as an example would indeed give a very high
confidence level in my model.
Robert-Jan Veldhuizen writes:
I think there's not much need for setting the JSD to stop a move at, to
values like 3 or higher even, when you'd settle for >1.65 JSD otherwise.
As long as you keep the minimum trials value high enough, like 216 or
higher when the moves are close or SE's are high.
Part of the reason is that GNUBG keeps measuring the JSD diff even after
a certain rollout candidate has stopped. Should the JSD drop below your
set value later, GNUBG will re-start the interrupted rollout of the
trial(s) and get them up to the current number of trials, then proceed
If you're planning on doing 1296 or maybe 2592 trials maximum, my idea
is that setting it at minimum 324 trials with 2.2 JSD is enough, if
you'd settle for 1.65 JSD after the maximum number of trials.
Very hard to really test or model this though...
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