[Computer-go] AlphaZero tensorflow implementation/tutorial

uurtamo uurtamo at gmail.com
Sun Dec 9 17:51:08 PST 2018


A "scoring estimate" by definition should be weaker than the computer
players it's evaluating until there are no more captures possible.

Yes?

s.

On Sun, Dec 9, 2018, 5:49 PM uurtamo <uurtamo at gmail.com wrote:

> By the way, why only 40 moves? That seems like the wrong place to
> economize, but maybe on 7x7 it's fine?
>
> s.
>
> On Sun, Dec 9, 2018, 5:23 PM cody2007 via Computer-go <
> computer-go at computer-go.org wrote:
>
>> Thanks for your comments.
>>
>> >looks you made it work on a 7x7 19x19 would probably give better result
>> especially against yourself if you are a complete novice
>> I'd expect that'd make me win even more against the algorithm since it
>> would explore a far smaller amount of the search space, right?
>> Certainly something I'd be interested in testing though--I just would
>> expect it'd take many months more months of training however, but would be
>> interesting to see how much performance falls apart, if at all.
>>
>> >for not cheating against gnugo, use --play-out-aftermath of gnugo
>> parameter
>> Yep, I evaluate with that parameter. The problem is more that I only play
>> 20 turns per player per game. And the network seems to like placing stones
>> in terrotories "owned" by the other player. My scoring system then no
>> longer counts that area as owned by the player. Probably playing more turns
>> out and/or using a more sophisticated scoring system would fix this.
>>
>> >If I don't mistake a competitive ai would need a lot more training such
>> what does leela zero https://github.com/gcp/leela-zero
>> Yeah, I agree more training is probably the key here. I'll take a look at
>> leela-zero.
>>
>> ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
>> On Sunday, December 9, 2018 7:41 PM, Xavier Combelle <
>> xavier.combelle at gmail.com> wrote:
>>
>> looks you made it work on a 7x7 19x19 would probably give better result
>> especially against yourself if you are a complete novice
>>
>> for not cheating against gnugo, use --play-out-aftermath of gnugo
>> parameter
>>
>> If I don't mistake a competitive ai would need a lot more training such
>> what does leela zero https://github.com/gcp/leela-zero
>> Le 10/12/2018 à 01:25, cody2007 via Computer-go a écrit :
>>
>> Hi all,
>>
>> I've posted an implementation of the AlphaZero algorithm and brief
>> tutorial. The code runs on a single GPU. While performance is not that
>> great, I suspect its mostly been limited by hardware limitations (my
>> training and evaluation has been on a single Titan X). The network can beat
>> GNU go about 50% of the time, although it "abuses" the scoring a little
>> bit--which I talk a little more about in the article:
>>
>>
>> https://medium.com/@cody2007.2/alphazero-implementation-and-tutorial-f4324d65fdfc
>>
>> -Cody
>>
>> _______________________________________________
>> Computer-go mailing listComputer-go at computer-go.orghttp://computer-go.org/mailman/listinfo/computer-go
>>
>>
>> _______________________________________________
>> Computer-go mailing list
>> Computer-go at computer-go.org
>> http://computer-go.org/mailman/listinfo/computer-go
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://computer-go.org/pipermail/computer-go/attachments/20181209/e29643e5/attachment.html>


More information about the Computer-go mailing list