Thanks Rémi's clarification. I forgot that 6,000-7,000 playouts per second
is with supporting weighted moves globally and incrementally updating
various data structures, not pure uniform random playouts. If I recall
correctly, Lukasz's implementation was 1,000-2,000 playouts per second
faster than mine.
Aja
-----原始郵件-----
From: Rémi Coulom
Sent: Friday, October 28, 2011 2:33 PM
To: [email protected]
Subject: Re: [Computer-go] MCTS playouts per second
I think it is more like 100k on 9x9, and 25k on 19x19
http://www.mail-archive.com/[email protected]/msg11214.html
Rémi
On 28 oct. 2011, at 06:30, Aja Huang wrote:
No, I meant 6,000-7,000 playouts per second on 19x19.
Aja
From: Michael Williams
Sent: Thursday, October 27, 2011 5:18 PM
To: [email protected]
Subject: Re: [Computer-go] MCTS playouts per second
Perhaps you meant to say 60,000-70,000 playouts per second for libego?
On Wed, Oct 26, 2011 at 10:23 PM, Aja Huang <[email protected]> wrote:
On 19x19, Erica's speed is around 5,500 lightweight playouts per second on
a single i7 cpu. As far as I know, Lukasz Lew's libego, which is open
source, is the fastest implementation of MCTS and can reach around
6,000-7,000 lightweight playouts per second in the same cpu.
Aja
-----原始郵件----- From: Scott Christensen
Sent: Wednesday, October 26, 2011 6:48 AM
To: [email protected]
Subject: [Computer-go] MCTS playouts per second
Just want to check what the expected playout performance is of well
tuned monte-carlo engines? My MCTS engine is averaging apx 3,500
lightweight playouts per second on a single i5 32 bit cpu. Any
suggestions on very efficient source code examples for fast
monte-carlo playouts?
I've spent a lot of time comparing recursive group formation vs
non-recursive but it doesn't seem to make a big difference. It seems
that updating the list of likely moves after every play with something
similar to the mogo probability rules is the most time consuming part
as I currently recalculate the probabilities of moves at every empty
point on the board each turn. It seems necessary if one doesn't want
to handle all the exceptions to keeping the previous turn's play
probabilities.
Also any thoughts on combining pattern scoring and other conventional
techniques together with a UCT tree? If two branches have very
similar simulated win ratios could one use other factors to choose the
best branch? It seems if there is a very wide branching such as at
the beginning of the game, there is a lot of room to add other
heuristics to choosing the best move when monte-carlo scores are
within range of expected error.
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