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Show HN: Play Go Against a Deep Neural Network

chrisc36.github.io

11–20 of 33 posts

Re: Show HN: Play Go Against a Deep Neural Network

#11
While other commenters only came here to say "I am a bad player, but I won" I want to say I have lost.

The bot was better than me in the sense that he knew exactly the best thing to do in "standard" situations and I don't. In a sense he looks more experienced and studied than me. But he did some stupid moves in some other situations in which he had to think more as a human, I imagine.

Re: Show HN: Play Go Against a Deep Neural Network

#12
post #4
post #2

There doesn't seem to be a way to pass. How do you end the game? As a very bad Go player, the bot managed to be even worse than me in several key engagements, giving me territory I had no right to capture. It still beat me by a little bit, though (I think -- the game never actually ended!) I wonder if complementing the NN with a more traditional tree search would help it play better tactically. EDIT: Also, your paper…

Thanks for catching that, I fixed the link. The network itself has no capability to pass its turn, which is a consequence of the fact it was only trained to predict player moves, not passes (we thought trying to learn when to pass would be difficult and a complication best avoided). So essentially you have to play until it seems clear to you the position is won or lost. If you played on indefinitely the DCNN would st…

But it doesn't seem possible on the web link for the player to pass and then see the score?

Re: Show HN: Play Go Against a Deep Neural Network

#17
post #7
post #3

It plays the opening and middle game impressively well - provided one plays reasonable moves. I'm a 4 dan amateur player and it had me beat around move 40. Its strength deteriorates to a complete beginner fairly quickly whenever there's a fight or a nonstandard situation.

That's about what I would expect. The two major weaknesses of "naively" training a Go player in this no-look-ahead purely-supervised way are: 1) The training data only consists of positions that occurred in professional games. This means positions that are not likely to occur in that context have no training data, making the network liable to play poorly. 2) The lack of any kind of planning ahead means situations tha…

This is quite exciting. I'd imagine combining the supervised version with the usual Monte Carlo search would result in a huge jump in strength, and that doesn't seem too difficult of a task to do. Normally the Monte Carlo bot has a strong fighting ability, but about 0 knowledge of fuseki. This one seems to have mid-high level dan in fuseki, and yet if you get a ladder, you win the game.

Re: Show HN: Play Go Against a Deep Neural Network

#18
http://www.computer-go.info/ has lots of info on other bots and is run by Nick Wedd who is an admin on KGS and is involved in running computer go tournaments. You can play vs lots of different bots (some of which are virtually guaranteed to beat you, but not quite...) on KGS.

Re: Show HN: Play Go Against a Deep Neural Network

#20
post #12
post #4

Earlier quoted context omitted.

Thanks for catching that, I fixed the link. The network itself has no capability to pass its turn, which is a consequence of the fact it was only trained to predict player moves, not passes (we thought trying to learn when to pass would be difficult and a complication best avoided). So essentially you have to play until it seems clear to you the position is won or lost. If you played on indefinitely the DCNN would st…

But it doesn't seem possible on the web link for the player to pass and then see the score?

No, you'll have to estimate or count manually, as it stands. An "end game now" button sounds like a reasonable fix, though.
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