Re: [Computer-go] AlphaGo Zero

About the fact that ladders appeared so late: - The learning was based on self-play. Understanding ladders is perhaps not so important if your opponent doesn't understand them either... Every time a decisive ladder appears on the board, the result is practically a coin toss. - And as others have

Re: [Computer-go] AlphaGo Zero

I saw my first AlphaGo Zero joke today: After a few more months of self-play the games might look like this: AlphaGo Zero Black - move 1 AlphaGo Zero White - resigns Cheers, David G Doshay ddos...@mac.com ___ Computer-go mailing list

Re: [Computer-go] AlphaGo Zero

I also expected bootstrapping by self-play. (I also wrote a post to that effect. But of course, DeepMind actually DID IT.) But I didn't envision any of the other stuff. This is why I love their papers. Papers from most sources are predictable, skimpy, and sketchy, but theirs contain all sorts

Re: [Computer-go] AlphaGo Zero

Some thoughts toward the idea of general game-playing... One aspect of Go is ideally suited for visual NN: strong locality of reference. That is, stones affect stones that are nearby. I wonder whether the late emergence of ladder understanding within AlphaGo Zero is an artifact of the board

Re: [Computer-go] AlphaGo Zero

Yeah, I would expect that encoding stones as "signed liberty count" would train faster/better/stronger. You could imagine a follow-up paper where Go features are supplied. But, if I think about this on a large scale, wouldn't it be huge to put together a general game-playing program just based

Re: [Computer-go] AlphaGo Zero

On 18/10/2017 22:00, Brian Sheppard via Computer-go wrote: > This paper is required reading. When I read this team’s papers, I think > to myself “Wow, this is brilliant! And I think I see the next step.” > When I read their next paper, they show me the next *three* steps. Hmm, interesting way of

Re: [Computer-go] AlphaGo Zero

On 18/10/2017 22:00, Brian Sheppard via Computer-go wrote: > A stunning result. The NN uses a standard vision architecture (no Go > adaptation beyond what is necessary to represent the game state). The paper says that Master (4858 rating) uses Go specific features, initialized by SL, and the same

Re: [Computer-go] AlphaGo Zero

On 18/10/2017 19:50, cazen...@ai.univ-paris8.fr wrote: > > https://deepmind.com/blog/ > > http://www.nature.com/nature/index.html Select quotes that I find interesting from a brief skim: 1) Using a residual network was more accurate, achieved lower error, and improved performance in AlphaGo by

Re: [Computer-go] AlphaGo Zero

This paper is required reading. When I read this team’s papers, I think to myself “Wow, this is brilliant! And I think I see the next step.” When I read their next paper, they show me the next *three* steps. I can’t say enough good things about the quality of the work. A stunning result.

Re: [Computer-go] AlphaGo Zero

A link to the paper (from the blog post): https://deepmind.com/documents/119/agz_unformatted_nature.pdf Enjoy! Álvaro. On Wed, Oct 18, 2017 at 2:29 PM, Richard Lorentz wrote: > Wow! That's very exciting. I'm glad they didn't completely shelve the > project as they

Re: [Computer-go] AlphaGo Zero

They are 80 games of different version of alphago and 3 of alphago against same version of alphago in supplementary data of https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html#supplementary-information Le 18/10/2017 à 20:29, Richard Lorentz a écrit : > Wow! That's very

Re: [Computer-go] AlphaGo Zero

Wow! That's very exciting. I'm glad they didn't completely shelve the project as they implied they might do after the match with Lee Sedol. I'm looking forward to seeing some games and "... plays unknown to humans", as Hassabis states. Also, I love this comment from Silver, something I have

[Computer-go] AlphaGo Zero

https://deepmind.com/blog/ http://www.nature.com/nature/index.html Impressive! ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go