So I will start to create software, and if someone want to use it you will be free as free software, and I already found someone
who is ready to host the server side. From a practical point of view, I will use public key signing to distribute go software (binary or source), so I will ask the author to sign it and give me their public key. Xavier Combelle Le 12/01/2017 à 11:04, Gian-Carlo Pascutto a écrit : > On 11-01-17 18:09, Xavier Combelle wrote: >> Of course it means distribute at least the binary so, or the source, >> so proprietary software could be reluctant to share it. But for free >> software there should not any problem. If someone is interested by my >> proposition, I would be pleased to realize it. > It is obvious that having a 30M dataset of games between strong players > (i.e. replicating the AlphaGo training set) would be beneficial to the > community. It is clear that most of us are trying to do the same now, > that is somehow trying to learn a value function from the about ~1.5M > KGS+Tygen+GoGoD games while trying to control overfitting via various > measures. (Aya used small network + dropout. Rn trained multiple outputs > on a network of unknown size. I wonder why no-one tried normal L1/L2 > regularization, but then I again I didn't get that working either!) > > Software should also not really be a problem: Leela is free, Ray and > Darkforest are open source. If we can use a pure DCNN player I think > there are several more options, for example I've seen several programs > in Python. You can resolve score disagreement by invoking GNU Go --score > aftermath. > > I think it's an open question though, *how* the games should be > generated, i.e.: > > * Follow AlphaGo procedure but with SL instead of RL player (you can use > bigger or smaller networks too, many tradeoffs possible) > * Play games with full MCTS search and small number of playouts. (More > bias, much higher quality games). > * The author of Aya also stated his procedure. > * Several of those and mix :-) >
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