[Computer-go] MCTS playouts per second

Aja Huang ajahuang at gmail.com
Thu Oct 27 21:30:16 PDT 2011


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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