[Computer-go] MCTS playouts per second

Brian Sheppard sheppardco at aol.com
Wed Oct 26 16:23:34 PDT 2011


So-called "light" playouts might be 10x to 20x that speed. I am not sure,
but a lot faster.

"Heavy" playouts should be 2x at least. Pebbles is ~5k/sec on a single core
of a first-generation dual core CPU. Fuego, Mogo are comparable to Pebbles,
Pachi is slower but probably smarter.

Look at libego for a fast light implementation. Fuego and Pachi for good
heavy implementations.



-----Original Message-----
From: computer-go-bounces at dvandva.org
[mailto:computer-go-bounces at dvandva.org] On Behalf Of Scott Christensen
Sent: Wednesday, October 26, 2011 8:48 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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