I have concluded that AGZ's policy of resigning "lost" games early is somewhat significant. Not as significant as using residual networks, for sure, but you wouldn't want to go without these advantages.
The benefit cited in the paper is speed. Certainly a factor. I see two other advantages. First is that training does not include the "fill in" portion of the game, where every move is low value. I see a specific effect on the move ordering system, since it is based on frequency. By eliminating training on fill-ins, the prioritization function will not be biased toward moves that are not relevant to strong play. (That is, there are a lot of fill-in moves, which are usually not best in the interesting portion of the game, but occur a lot if the game is played out to the end, and therefore the move prioritization system would predict them more often.) My ad hoc alternative is to not train on positions after the first pass in a game. (Note that this does not qualify as "zero knowledge", but that is OK with me since I am not trying to reproduce AGZ.) Second is the positional evaluation is not training on situations where everything is decided, so less of the NN capacity is devoted to situations in which nothing can be gained. As always, YMMV. Best, Brian _______________________________________________ Computer-go mailing list [email protected] http://computer-go.org/mailman/listinfo/computer-go
