Hi,
Thank you for the paper.
Not only next move, but also opponent move and next counter move
prediction is very interesting.
I have two questions.
darkforest : standard features, 1 step prediction on KGS dataset
darkfores1 : extended features, 3 step prediction on GoGoD dataset
darkfores2 : fine-tuned the learning rate, Based on darkfores1
1. How did you tune learning rate in darkfores2?
2. darkfores1 is stronger than darkforest. Is it because of 3 step
prediction or using GoGoD? Do you have a result using
"standard features, 1 step prediction on GoGoD"?
Regards,
Hiroshi Yamashita
----- Original Message -----
From: "Yuandong Tian" <yuandong.t...@gmail.com>
To: <computer-go@computer-go.org>
Sent: Wednesday, November 25, 2015 5:45 AM
Subject: Re: [Computer-go] Facebook Go AI.
Hi all,
I am the first author of Facebook Go AI. Thanks for your interest! This is
the first time I post a message here, so please forgive me if I mess up
with anything.
1. The estimation of 1d-2d is based on the win rate of free game in the
last 3 months (since darkforest launched in Aug). See Table 6 in the paper.
For ranked game, its rank is definitely lower since people tend to play
more seriously. It seems that now darkforest is 1k and darkfores1 is 1d.
2. Here is the Pachi 10k command line for no pondering.
pachi -t =10000 threads=8,pondering=0
For pondering, it is simply
pachi -t =10000 threads=8
In both cases, all the spatial patterns are properly loaded. See the
following GTP response:
W>> protocol_version
Random seed: 1448000132
Loaded spatial dictionary of 1064482 patterns.
Loaded 3021829 pattern-probability pairs.
3. We use pachi version 11.99 as shown in the following GTP response:
W>> version
W<< = 11.99 (Genjo-devel): If you believe you have won but I am still
playing, please help me understand by capturing all dead stones. Anyone can
send me 'winrate' in private chat to get my assessment of the position.
Have a nice game!
4. Darkfores2 is still DCNN model and no search is involved.
Thanks! If you have any comments, please let me know.
----------------------------
Yuandong Tian
Research Scientist,
Facebook Artificial Intelligence Research (FAIR)
Website:
https://research.facebook.com/researchers/1517678171821436/yuandong-tian/
--------------------------------------------------------------------------------
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