I use a method inititally from the Mogo team that sorts of randomizes
the position before running the heavy playout. One simply plays
uniformly random *non contact* moves. The effect of this is that it
preserves the shapes of stones on the board, but it prevents the heavy
playouts from
I have to add that it is possible that a large part of the advantage
from using heavy playouts in valkyria comes from using the same code
to bias the the exploration part of MCTS.
I could probably test it by simply relying completely on AMAF with the
proximity heuristic as the only bias.
So you say that: ...I'm observing that most of the increase in level
comes from the selection during exploration and only in small part
from the selection during simulation., could you elaborate at all?
This is very interesting. That almost suggests it might be fruitful
to use the patterns
On Sat, Nov 15, 2008 at 11:38:34PM +0100, [EMAIL PROTECTED] wrote:
Being a computer scientist but new to go, i can grasp some of the theory.
The question I was trying to get across was:
In a game of self play, if both parties are employing only monte carlo,
surely its not a good conceptual
On Sunday 16 November 2008, Heikki Levanto wrote:
On Sat, Nov 15, 2008 at 11:38:34PM +0100, [EMAIL PROTECTED] wrote:
Being a computer scientist but new to go, i can grasp some of the theory.
The question I was trying to get across was:
In a game of self play, if both parties are employing
Hello Heikki,
Heikki Levanto: [EMAIL PROTECTED]:
On Sat, Nov 15, 2008 at 11:38:34PM +0100, [EMAIL PROTECTED] wrote:
Being a computer scientist but new to go, i can grasp some of the theory.
The question I was trying to get across was:
In a game of self play, if both parties are employing
On Sun, Nov 16, 2008 at 11:46:28AM +, D Gilder wrote:
This is the way I understand the random playouts: If, in a given position,
white is clearly ahead, he will win the game if both parts play perfect
moves. He is also likely to win if both parts play reasonably good moves
(say, like
Quoting Hideki Kato [EMAIL PROTECTED]:
Heikki Levanto: [EMAIL PROTECTED]:
The way I understand it, modern Monte Carlo programs do not even try to
emulate a human player with a random player - obviously that would not work.
I believe CrazyStone's use of patterns does so and it seems
The random playouts or even heavy playouts are not intended to emulate
a human player. Heikki is exactly right.
It's a crude measurement of how good the position is. The moves in a
random playout are horrible and so are the moves in a heavy playout.
In fact, improving them arbitrarily
Yes, Valkyria does a lot of ladder reading as well. Actually pattern
matching in the case of Valkyria is a little unclear, it is a
decision trees where the leaves are often procedure calls that looks
at a larger portion of the board. The ladder code is called for
various reasons in the
-
From: [EMAIL PROTECTED] [mailto:computer-go-
[EMAIL PROTECTED] On Behalf Of Magnus Persson
Sent: Sunday, November 16, 2008 5:45 AM
To: computer-go@computer-go.org
Subject: Re: [computer-go] FW: computer-go] Monte carlo play?
Quoting Hideki Kato [EMAIL PROTECTED]:
Heikki Levanto
Some months ago I did several experiments with using tactics and
patterns in playouts. Generally I found a big boost in strength using
tactics. I also found a boost in strength using patterns but with a
severe diminishing return after a certain number and even becoming
detrimental when
So you say that: ...I'm observing that most of the increase in level
comes from the selection during exploration and only in small part
from the selection during simulation., could you elaborate at all?
This is very interesting. That almost suggests it might be fruitful
to use the patterns in the
On 17-nov-08, at 02:42, George Dahl wrote:
So you say that: ...I'm observing that most of the increase in level
comes from the selection during exploration and only in small part
from the selection during simulation., could you elaborate at all?
This is very interesting. That almost suggests
I look forward to hearing more! Happy testing.
- George
On Sun, Nov 16, 2008 at 11:53 PM, Mark Boon [EMAIL PROTECTED] wrote:
On 17-nov-08, at 02:42, George Dahl wrote:
So you say that: ...I'm observing that most of the increase in level
comes from the selection during exploration and only
It seems move selection in the playouts should be very random at first and more
deterministic toward the end of the playout. Has anyone tried that?
Mark Boon wrote:
On 17-nov-08, at 02:42, George Dahl wrote:
So you say that: ...I'm observing that most of the increase in level
comes from
In a game of self play, if both parties are employing only monte
carlo, ... random simulations... wouldnt it be very weak...
... and some playing around I am clearly mistaken because its works
quite well.
Yes, it doesn't make sense but it does indeed seem to work :-).
I have seen papers
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