> ladders, not just liberties. In that case, yes! If you outright tell the > neural net as an input whether each ladder works or not (doing a short > tactical search to determine this), or something equivalent to it, then the > net will definitely make use of that information, ...
Each convolutional layer should spread the information across the board. I think alpha zero used 20 layers? So even 3x3 filters would tell you about the whole board - though the signal from the opposite corner of the board might end up a bit weak. I think we can assume it is doing that successfully, because otherwise we'd hear about it losing lots of games in ladders. > something the first version of AlphaGo did (before they tried to make it > "zero") and something that many other bots do as well. But Leela Zero and > ELF do not do this, because of attempting to remain "zero", ... I know that zero-ness was very important to DeepMind, but I thought the open source dedicated go bots that have copied it did so because AlphaGo Zero was stronger than AlphaGo Master after 21-40 days of training. I.e. in the rarefied atmosphere of super-human play that starter package of human expert knowledge was considered a weight around its neck. BTW, I agree that feeding the results of tactical search in would make stronger programs, all else being equal. But it is branching code, so much slower to parallelize. Darren _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
