On 11-01-17 14:33, Kensuke Matsuzaki wrote: > Hi, > > I couldn't get positive experiment results on Ray. > > Rn's network structure of V and W are similar and share parameters, > but only final convolutional layer are different. > I trained Rn's network to minimize MSE of V(s) + W(s). > It uses only KGS and GoGoD data sets, no self play with RL policy.
How do you get the V(s) for those datasets? You play out the endgame with the Monte Carlo playouts? I think one problem with this approach is that errors in the data for V(s) directly correlate to errors in MC playouts. So a large benefit of "mixing" the two (otherwise independent) evaluations is lost. This problem doesn't exist when using raw W/L data from those datasets, or when using SL/RL playouts. (But note that using the full engine to produce games *would* suffer from the same correlation. That might be entirely offset by the higher quality of the data, though.) > But I have no idea about how to use V(s) or v(s) in MCTS. V(s) seems potentially useful for handicap games where W(s) is no longer accurate. I don't see any benefit over W(s) for even games. -- GCP _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
