No need for AlphaGo hardware to find out; any toy problem will suffice to explore different initialization schemes... The main benefit of starting random is to break symmetries (otherwise individual neurons cannot specialize), but there are other approaches that can work even better. Further you typically want to start with small weights so that the initial mapping is relatively smooth.
E. On Tue, Nov 21, 2017 at 2:24 PM, "Ingo Althöfer" <3-hirn-ver...@gmx.de> wrote: > AlphaGo Zero started with random values in > its neural net - and reached top level > within 72 hours. > > Would it typically help or disrupt to start > instead with values that are non-random? > What I have in mind concretely: > > Look at 19x19 Go with komi=5.5 > In run A you start with random values in the net. > In another run B you start with the values that had > emerged in the 7.5-NN after 72 hours. > > Would typically A or B learn better? > Would there be a danger that B would not be able > to leave the 7.5-"solution"? > > It is a pity that I/we do not have the hardware of > AlphaGo Zero at hand for such experiments. > > Ingo. > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go
_______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
