[Computer-go] Aya reaches pro level on GoQuest 9x9 and 13x13

Detlef Schmicker ds2 at physik.de
Sat Nov 19 00:07:30 PST 2016


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Hi Hiroshi,

thanks a lot for your info.

You did not try reinforcement learning I think. Do you have any idea,
why this would make the policy network 250ELO stronger, as mentioned
in the alphago paper (80% winrate)?

Are pros playing so bad?

Do you think playing strength would be better, if one only takes into
account the moves of the winning player?

Detlef

Am 19.11.2016 um 05:18 schrieb Hiroshi Yamashita:
> Hi,
> 
>> Did you not find a benefit from a larger value network? Too
>> little data and too much overfitting? Or more benefit from more
>> frequent evaluation?
> 
> I did not find larger value network is better. But I think I need
> more taraining data and stronger selfplay. I did not find
> overfitting so far, and did not try more frequent evaluation.
> 
>>> Policy + Value vs Policy, 1000 playouts/move, 1000 games. 9x9,
>>> komi 7.0 0.634  using game result. 0 or 1
>> 
>> I presume this is a winrate, but over what base? Policy network?
> 
> Yes. Policy network(only root node) + value network  vs  Policy
> network(only root node).
> 
>> How do you handle handicap games? I see you excluded them from
>> the KGS dataset. Can your value network deal with handicap?
> 
> I excluded hadicap games. My value network can not handle hadicaps.
> It it only for komi 7.5.
> 
> Thanks, Hiroshi Yamashita
> 
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