DeepMind has published a number of papers on how to stabilize RL strategies
in a landscape of nontransitive cycles. See
https://papers.nips.cc/paper/2018/file/cdf1035c34ec380218a8cc9a43d438f9-Paper.pdf
I haven't fully digested the paper, but what I'm getting from it is that if
you want your evalua
> ladders, not just liberties. In that case, yes! If you outright tell the
> neural net as an input whether each ladder works or not (doing a short
> tactical search to determine this), or something equivalent to it, then the
> net will definitely make use of that information, ...
Each convolution
The primary purpose of a rating system is to predict the results of future
games accurately (this is the usual axiom, at least).
In a one-dimensional rating system, such as Elo, where each player's skill
is represented by a single number, it is impossible to have a (non-wacky)
system where A is ex
also frankly not a problem for a rating system to handle.
a rating system shouldn't be tweaked to handle eccentricities of its
players other than the general assumptions of how a game's result is
determined (like, does it allow for "win" and "draw" and "undetermined" or
just "win").
s.
On Fri,
@Claude - Oh, sorry, I misread your message, you were also asking about
ladders, not just liberties. In that case, yes! If you outright tell the
neural net as an input whether each ladder works or not (doing a short
tactical search to determine this), or something equivalent to it, then the
net wil
Hi Claude - no, generally feeding liberty counts to neural networks doesn't
help as much as one would hope with ladders and sekis and large capturing
races.
The thing that is hard about ladders has nothing to do with liberties - a
trained net is perfectly capable of recognizing the atari, this is
On Fri, Jan 22, 2021 at 3:45 AM Hiroshi Yamashita wrote:
> This kind of joseki is not good for Zero type. Ladder and capturing
> race are intricately combined. In AlphaGo(both version of AlphaGoZero
> and Master) published self-matches, this joseki is rare.
> -
On Fri, Jan 22, 2021 at 8:08 AM Rémi Coulom wrote:
> You are right that non-determinism and bot blind spots are a source of
> problems with Elo ratings. I add randomness to the openings, but it is
> still difficult to avoid repeating some patterns. I have just noticed that
> the two wins of Crazy
Hi. Maybe it's a newbie question, but since the ladders are part of the
well defined topology of the goban (as well as the number of current
liberties of each chain of stone), can't feeding those values to the
networks (from the very start of the self teaching course) help with
large shichos an
Hi David,
You are right that non-determinism and bot blind spots are a source of
problems with Elo ratings. I add randomness to the openings, but it is
still difficult to avoid repeating some patterns. I have just noticed that
the two wins of CrazyStone-81-15po against LZ_286_e6e2_p400 were caused
Hi,
The most noticeable case of this is with Mi Yuting's flying dagger joseki.
I'm not familiar with this.
I found Hirofumi Ohashi 6d pro's explanation half year ago in HCCL ML.
The following is a quote.
-
https://gokifu.net/t2.php?s=
One tricky thing is that there are some major nonlinearities between
different bots early in the opening that break Elo model assumptions quite
blatantly at these higher levels.
The most noticeable case of this is with Mi Yuting's flying dagger joseki.
I've noticed for example that in particular m
Thanks for computing the new rating list.
I feel it did not fix anything. The old Zen, cronus, etc.have almost no
change at all.
So it is not a good fix, in my opinion. No need to change anything to the
official ratings.
The fundamental problem seems that the Elo rating model is too wrong for
th
Hi,
This is original BayesElo. I updated manually. This is latest.
http://www.yss-aya.com/cgos/19x19/bayes.html
CrazyStone-18.044065
CrazyStone-81b-TiV 4032
Zen-15.7-3c1g 3999
CrazyStone-57-TiV 3618
This renames CrazyStone-57-TiV to CrazyStone-18.04.
http://www.yss-aya.com/cgos/19x1
I checked, and CrazyStone-57-TiV is using the same neural network and
hardware as CrazyStone-18.04. Batch size, cuDNN version, and time
management heuristics may have changed, but I expect that strength should
be almost identical. CrazyStone-57-TiV may be a little stronger.
So it seems that the ra
Hi,
The Elo statistical model is wrong when different kind of programs play against
I have a similar experience.
I calculated Japanese Shogi women pro rating before.
The strongest woman, Ichiyo Shimizu, her rating is 1578 Elo.
Her winrate against men pros is 18%(163 games), and against women p
It's a relative ranking versus who you actually get to play against.
Sparsity of actual skill will lead to that kind of clumping.
The only way that a rating could meaningfully climb by playing gnugo or
your direct peers is going to happen exponentially slowly -- you'd need to
lose to gnugo twice
Hi,
Thanks to you for taking care of CGOS.
I have just connected CrazyStone-57-TiV. It is not identical, but should be
similar to the old CrazyStone-18.04. CrazyStone-18.04 was the last version
of my program that used tensorflow. CrazyStone-57 is the first neural
network that did not use tensorfl
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