I was thinking about bootstrapping possibilities, and wondered whether it would be possible to use a shallower mimic net for positional evaluation playouts from a specific depth on after having generated positions with a certain branching factor that typically allows the actual pro move to be included, hopefully finding even stronger moves, which then are fed back as targets for the primary function/net. Perhaps even apply different amounts of shallowness in mimic function NN configuration as well as depth/branching for move tree generation.
No idea if there are kind of depth/branching configurations that would make sense or seem promising, given the existing hardware options. On Sun, Mar 15, 2015 at 2:56 AM, Hugh Perkins <hughperk...@gmail.com> wrote: > To be honest, what I really want is for it to self-learn, like David > Silver's TreeStrap did for chess, but on the one hand I guess I should > start by reproducing the existent, and on the other hand if we need > millions of moves to train the net, that's going to make for very slow > self-play... Also, David Silver was associated with Aja Huang's > paper, and I'm guessing therefore that it is very non-trivial to do, > otherwise David Silver would have done it already :-) > _______________________________________________ > 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
