[Computer-go] MCTS playouts per second

Brian Sheppard sheppardco at aol.com
Wed Oct 26 19:40:23 PDT 2011


Clarification: the numbers that I quoted on my previous post were on 9x9. 


-----Original Message-----
From: computer-go-bounces at dvandva.org
[mailto:computer-go-bounces at dvandva.org] On Behalf Of Aja Huang
Sent: Wednesday, October 26, 2011 10:23 PM
To: computer-go at dvandva.org
Subject: Re: [Computer-go] MCTS playouts per second

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

-----????-----
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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