[Computer-go] MCTS playouts per second

Aja Huang ajahuang at gmail.com
Fri Oct 28 14:22:10 PDT 2011


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: computer-go at dvandva.org
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/computer-go@computer-go.org/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: computer-go at dvandva.org
> 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 <ajahuang at gmail.com> 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: computer-go at dvandva.org
> 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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