Yamashita san, About your question, I think that the answer is yes.
AlphaZero Symmetries seems successfully saturated. That means that 20b neural network with symmetries has a capacity to learn at most 21M full games. If you let the network to learn 21M full games without preprocessing inputs for symmetries, the network may over-fit by breaking symmetries since the input data for training are too small (1/8). So they generated more games in exchange for the preprocessing. I agree with you that they could not remove domain dependent knowledge completely. Thinning out positions of each game for game symmetries may be important. I have no knowledges about generalization of symmetries. It sounds hard problem if you don't preprocess training inputs. - ICHIKAWA, Yuji > 2019/04/04 23:34、Hiroshi Yamashita <y...@bd.mbn.or.jp>のメール: > > Hi Ichikawa san, > > Thank you for nice explanation. I think your guess is maybe right. > And 2018 nature paper might have no mistake. > > I had checked carefully both Figure 1. > > 1. 2017 reaches AlphaGo Lee in 170,000 step. 2018 reaches in 80,000 step. > 2. 2017 and 2018 reach "AlphaGo Zero(20 block)" in similar steps. > 3. Final strength is similar. > > So I had thought "If you use 7 times games record, initial learning speed is > fast, > but final strength is similar.". > So maybe they want to say "21 million Training Games is enough." > > But it is wrong. > In Go, if you use all positions from a game, it makes overfitting? And > learning will fail? > Without symmery-augmented, Go can use only 20 positions from a game. > Chess and Shogi is ok. It looks like domain dependent... > > Thanks, > Hiroshi Yamashita > >> Go version in AlphaZero 2017 finished the training in 34 hours according to >> Table S3. >> And it looks like AlphaZero Symmetries in AlphaZero 2018 finished the >> training in the same time according to Figure S1. >> So I think that the authors had adopted AlphaZero Symmetries in 2017 paper >> by mistake and retried the experiment again in 2018 paper. >> In order to compensate symmetries with real self-plays, they generated 8 >> times more games and reduced positions per game to 1/8. >> It is just my guess^^ > _______________________________________________ > 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
