So the KGS bots darkforest and darkfores1 play with only DCNN, no MCTS
search added? I wish they would put darkfores2 with MCTS on KGS, why not
put your strongest bot out there?
2015-11-23 10:38 GMT-06:00 Petr Baudis <pa...@ucw.cz>:
> The numbers look pretty impressive! So this DNN is as strong as
> a full-fledged MCTS engine with non-trivial thinking time. The increased
> supervision is a nice idea, but even barring that this seems like quite
> a boost to the previously published results? Surprising that this is
> just thanks to relatively simple tweaks to representations and removing
> features... (Or is there anything important I missed?)
>
> I'm not sure what's the implementation difference between darkfores1 and
> darkfores2, it's a bit light on detail given how huge the winrate delta
> is, isn't it? ("we fine-tuned the learning rate") Hopefully peer review
> will help.
>
> Do I understand it right that in the tree, they sort moves by their
> probability estimate, keep only moves whose probability sum up to
> 0.8, prune the rest and use just plain UCT with no priors afterwards?
> The result with +MCTS isn't at all convincing - it just shows that
> MCTS helps strength, which isn't so surprising, but the extra thinking
> time spent corresponds to about 10k->150k playouts increase in Pachi,
> which may not be a good trade for +27/4.5/1.2% winrate increase.
>
> On Mon, Nov 23, 2015 at 09:54:37AM +0100, Rémi Coulom wrote:
> > It is darkforest, indeed:
> >
> > Title: Better Computer Go Player with Neural Network and Long-term
> > Prediction
> >
> > Authors: Yuandong Tian, Yan Zhu
> >
> > Abstract:
> > Competing with top human players in the ancient game of Go has been a
> > long-term goal of artificial intelligence. Go's high branching factor
> makes
> > traditional search techniques ineffective, even on leading-edge hardware,
> > and Go's evaluation function could change drastically with one stone
> change.
> > Recent works [Maddison et al. (2015); Clark & Storkey (2015)] show that
> > search is not strictly necessary for machine Go players. A pure
> > pattern-matching approach, based on a Deep Convolutional Neural Network
> > (DCNN) that predicts the next move, can perform as well as Monte Carlo
> Tree
> > Search (MCTS)-based open source Go engines such as Pachi [Baudis & Gailly
> > (2012)] if its search budget is limited. We extend this idea in our bot
> > named darkforest, which relies on a DCNN designed for long-term
> predictions.
> > Darkforest substantially improves the win rate for pattern-matching
> > approaches against MCTS-based approaches, even with looser search
> budgets.
> > Against human players, darkforest achieves a stable 1d-2d level on KGS Go
> > Server, estimated from free games against human players. This
> substantially
> > improves the estimated rankings reported in Clark & Storkey (2015), where
> > DCNN-based bots are estimated at 4k-5k level based on performance against
> > other machine players. Adding MCTS to darkforest creates a much stronger
> > player: with only 1000 rollouts, darkforest+MCTS beats pure darkforest
> 90%
> > of the time; with 5000 rollouts, our best model plus MCTS beats Pachi
> with
> > 10,000 rollouts 95.5% of the time.
> >
> > http://arxiv.org/abs/1511.06410
>
> --
> Petr Baudis
> If you have good ideas, good data and fast computers,
> you can do almost anything. -- Geoffrey Hinton
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