[Computer-go] Results from 19x19 Valkyria/1k H9 vs Valkyria/10k
Erik van der Werf
erikvanderwerf at gmail.com
Tue Oct 5 07:59:47 PDT 2010
Lukasz brings up an interesting point. Winrate may not be the ideal
statistic for all situations. Maybe the average score (as used in most
early work on MC), soft-max, or a median tracker would be better for
Maybe a nice question for the academics:
If you were free to keep track of a histogram for all possible scores
in each node (so you have everything from winrate at every possible
komi to simply the average score), then what would be the optimal
And if the above can be answered, what would be the minimum set of
statistics needed to maintain similar performance?
On Tue, Oct 5, 2010 at 4:08 PM, Aja <ajahuang at gmail.com> wrote:
> I think what Łukasz meant of "score" is the exact game scoring (such as
> Black wins 10 points, then return 10, rather than 1). He might have greater
> ambition to change the current architecture of MCTS that only 0/1 is used
> for the outcome of the simulations.
> ----- Original Message ----- From: <valkyria at phmp.se>
> To: <computer-go at dvandva.org>
> Sent: Tuesday, October 05, 2010 9:34 PM
> Subject: Re: [Computer-go] Results from 19x19 Valkyria/1k H9 vs Valkyria/10k
>> Quoting Łukasz Lew <lukasz.lew at gmail.com>:
>>> Your dynamic komi results are very convincing..
>>> But shouldn't we just concentrate on maximizing score instead of
>>> winning rate in the beginning of the game?
>> Maximizing winning rate means that the probability of having a score > 0
>> at the end of the game is maximum.
>> Exactly what do you mean with "maximizing score in the beginning of the
>> It is hard to estimate the score. Also in a game of go territory is not
>> everything. Aji and influence is also important. Win rate is as far as i
>> know the best way of capturing all these things in one measure that guides
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