>By the way, why only 40 moves? That seems like the wrong place to economize,
>but maybe on 7x7 it's fine?
I haven't implemented any resign mechanism, so felt it was a reasonable balance
to at least see where the players roughly stand. Although, I think I errored on
too few turns.
>A "scoring estimate" by definition should be weaker than the computer players
>it's evaluating until there are no more captures possible.
Not sure I understand entirely. But would agree that the scoring I use is
probably a limitation here.
‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Sunday, December 9, 2018 8:51 PM, uurtamo <uurt...@gmail.com> wrote:
> 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 <uurt...@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@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.combe...@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 list
>>>>> Computer-go@computer-go.org
>>>>>
>>>>> http://computer-go.org/mailman/listinfo/computer-go
>>>
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