With the recent upgrade to 10 block 128 filter net for LZ a few more people
have started running LZ bots on CGOS. I'm running the last 6x128 net as
LZ-b3b00c-t1-v3200, and it is using a recent build from the next branch
(v0.12-24-g4a77da5). I plan to make this an anchor that runs a long time. I
DCNNs are not magic but just non-linear continuous function
approximators with finite freedom and we can provide up to
10^8 samples (board positions) in practice.
Why do most people believe VN can approximate (perfect or
near perfect) value function? What do they estimate the
complexity of
Hi,
I am not convinced it is better. I guess rollouts can bring some additional
strength, especially in a CPU-only setting. I'll test this later.
For the moment, my main objective is shogi. I will participate in the World
Computer Shogi Championship in May. So I am developing a
At 06:31 AM 3/5/2018, valky...@phmp.se wrote:
>My guess is that there is some kind of threshold depending on the relative
>strength of MC eval and the value function of the NN.
My experiments suggest it's better to train with much longer MCTS
time than will be used in actual play, so the MCTS
On 5/03/2018 12:28, valky...@phmp.se wrote:
> Remi twittered more details here (see the discussion with gghideki:
>
> https://twitter.com/Remi_Coulom/status/969936332205318144
Thank you. So Remi gave up on rollouts as well. Interesting "difference
of opinion" there with Zen.
Last time I tested
On 5/03/2018 10:54, Dan wrote:
> I believe this is a problem of the MCTS used and not due
> to for lack of training.
>
> Go is a strategic game so that is different from chess that is full of
> traps.
Does the Alpha Zero result not indicate the opposite, i.e. that MCTS is
workable?
--
Remi twittered more details here (see the discussion with gghideki:
https://twitter.com/Remi_Coulom/status/969936332205318144
On 2018-03-05 10:16, Gian-Carlo Pascutto wrote:
On 28-02-18 07:13, Rémi Coulom wrote:
Hi,
I have just connected the newest version of Crazy Stone to CGOS. It
is
Actually prior to this it was trained with hundreds of thousands of
stockfish games and didn’t do well on tactics (the games were actually a
blunder fest). I believe this is a problem of the MCTS used and not due to
for lack of training.
Go is a strategic game so that is different from chess that
On 28-02-18 07:13, Rémi Coulom wrote:
> Hi,
>
> I have just connected the newest version of Crazy Stone to CGOS. It
> is based on the AlphaZero approach.
In that regard, are you still using Monte Carlo playouts?
--
GCP
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On 02-03-18 17:07, Dan wrote:
> Leela-chess is not performing well enough
I don't understand how one can say that given that they started with the
random network last week only and a few clients. Of course it's bad!
That doesn't say anything about the approach.
Leela Zero has gotten strong but
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