As of about an hour ago darkforest and darkfores1 have started playing
rated games on KGS!
2015-11-23 11:28 GMT-06:00 Andy <andy.olsen...@gmail.com>:
> 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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>> Computer-go@computer-go.org
>> http://computer-go.org/mailman/listinfo/computer-go
>
>
>
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