On 10-01-17 15:05, Hiroshi Yamashita wrote:
> Hi,
>
> Golois5 is KGS 4d.
> I think it is a first bot that gets 4d by using DCNN without search.
I found this paper:
https://openreview.net/pdf?id=Bk67W4Yxl
They are using residual layers in the DCNN.
--
GCP
Hi everyone. It occurs to me there might be a more efficient method to train
the value network directly (without using the policy network).
You are welcome to check my method: http://withablink.com/GoValueFunction.pdf
Let me know if there is any silly mistakes :)
I was writing code along those lines when AlphaGo debuted. When it became clear
that AlphaGo had succeeded, then I ceased work.
So I don’t know whether this strategy will succeed, but the theoretical merits
were good enough to encourage me.
Best of luck,
Brian
From: Computer-go
hi Bo,
> Let me know if there is any silly mistakes :)
You say "the perfect policy network can be
derived from the perfect value network (the best next move is the move
that maximises the value for the player, if the value function is
perfect), but not vice versa.", but a perfect policy for both
Very interesting,
but lets wait some days for getting an idea of the strength,
4d it reached due to games against AyaBotD3, now it is 3d again...
Detlef
Am 10.01.2017 um 15:29 schrieb Gian-Carlo Pascutto:
> On 10-01-17 15:05, Hiroshi Yamashita wrote:
>> Hi,
>>
>> Golois5 is KGS 4d.
>> I think
Hi,
Golois5 is KGS 4d.
I think it is a first bot that gets 4d by using DCNN without search.
Golois5 (4d) info says
"I use a policy network trained on Gogod games."
Golois4 (3d) info says
"I use a policy network trained on KGS games played by 6 dan or more."
If both network are same, GoGoD
