Thanks for the answers from both David and Magnus.
I had a win-rate of 3:1 after 50 games, I thought that was
significant enough. I'll do more testing and see if this was a fluke.
I expand one node at a time, I don't see what memory problems it may
cause so far. But my thinking times have been 5 sec. maximum and only
on 9x9 so I don't have that many nodes to fill a lot of memory. I'm
not going to sweat that part right now as I think the playouts will
get slower and reduce memory-usage by itself in time.
With pseudo-liberties I get 20K playouts/sec. (2Ghz Intel, 9x9 board)
That compares to 30K/sec. doing just playouts and no search. It
surprised me to see the tree-traversal takes this much time but it
may also be caused by trashing the cash and benchmarking the playouts
by themselves make things look too good because it'll all fits in the
cache.
With tactics the playouts during search drop to 4K/sec. Of course I
don't capture stones that can't escape. I haven't spent a lot of time
optimizing yet. I'd first rather experiment a lot more. At this point
the actual speed is not so important as long as I can see relative
improvements. My framework enables me to make and test different
variations easily. In the end I'd prefer to optimize the best one
instead of half a dozen of them.
What I did was to play deterministically during playout but use the
tactical information just to first select the tactical moves ahead of
the other during selection. The last part seems to give no gain at
best, which surprised me. I can see that its effect is low in nodes
that are visited often, but even those nodes are based on nodes
deeper in the tree that are not visited as often. I'll have to
investigate this further to know for sure what's going on.
Mark
On 5-feb-08, at 14:46, David Fotland wrote:
Hi Mark,
You should run a lot more test games. The 95% confidence interval
on the result is at least sqrt(1/num_games), so you need 400 or
more games to know the win rate within 5%. I’ve seen many
anomalous win rates when I used to test with 20 games. Now I use
200 games minimum, and I try to get 500 before I make any conclusion.
I think mogo is the only strong program that uses the UCB1-tuned
formula. The others use the same formula you use. I found a
thesis where they measured many different formulas and found little
difference. If any strong program other than mogo uses some
formula other than the basic one, can you please let us know?
The reason to only initialize the nodes after a certain count is to
save memory. The simple uct algorithm visits each child once
before using uct to choose one. If you create all child nodes
before any are visited you end up with most of the nodes in the
tree having zero visits.
How many playouts per second are you getting (from the start of the
game on a 9x9 board), and on what hardware?
Regards,
David
From: [EMAIL PROTECTED] [mailto:computer-go-
[EMAIL PROTECTED] On Behalf Of Mark Boon
Sent: Tuesday, February 05, 2008 7:54 AM
To: computer-go
Subject: [computer-go] More UCT / Monte-Carlo questions
Although most of my time has been eaten up by implementing/
improving some general framework parts I did get a chance to play a
bit with a simple UCT search. Some things that I found puzzled me a
bit and I hoped someone had an explanation or similar experiences.
I implemented a very basic UCT / MC program first using pseudo-
liberties. I figured this should be the base-line against which I
can test some ideas. To test if the program actually worked
properly I first let it play against Orego. The speed of my
playouts are similar to Orego so I figured the level of play should
be similar. (I switched off pondering and multiple-threading in
Orego to get an apples-to-apples comparison.)
To my surprise my program seemed to be winning the majority of the
games (after a few dozen games). When looking at Orego's output I
couldn't help noticing that at the start of the game it prints much
smaller numbers of 'runs' than my program, whereas by the end of
the game the numbers are similar. This may be the reason for my
program performing better. When I looked at the code of Orego I
noticed there are two main differences:
- It computes the UCT value in a completely different way. A
comment in the code refers to a paper called "Modification of UCT
with Patterns in Monte-Carlo Go". I haven't studied this yet, but
whatever it does it apparently doesn't do wonders over the standard
C * sqrt( (2*ln(N)) / (10*n) ) that I use.
- It only initialises the list of untried moves in the tree after a
node had a minimum run-count of 81 (on 9x9). For the life of me I
couldn't figure out what the effect of this was or what it actually
does. I was wondering if this has an effect of what is counted as a
'run' but I'm not sure.
Then I found a paragraph (4.2) in Remi Coulomn's paper about ELO
raings in patterns. It briefly describes it as "As soon as a number
of simulations is equal to the number of points on the board, this
node is promoted to internal node, and pruning is applied." I can't
help feeling that the implementation in Orego is doing this. But I
can't figure out where it does any pruning or applying patterns of
any kind. Is there supposedly a general benefit to this even
without pruning or patterns? As stated before, at least it doesn't
seem to provide any benefit over my more primitive implementation.
Maybe Peter Drake or someone else familiar with Orego knows more
about this?
Anyway, reading the same paragraph mentioned above again I was
struck by another detail I thought surprising: after doing the
required number of runs, the candidates are pruned to a certain
number 'n' based on patterns. Does that mean from then on the win-
ratio is ignored? What if the by far most successful move so far
does not match any pattern? Am I misunderstanding something here?
The paragraph is very brief and does not elaborate much detail.
On to my next step I introduced some very basic tactics to save
stones with one liberty, capture the opponent's stones with one
liberty and capturing the opponent's stones in a ladder. There are
many possible choices here. Just doing this near the last move and/
or over the whole board. Doing this in the simulation and/or during
the selection.
Just doing this near the last move during simulation caused a slow-
down of a factor 4 or 5 but improves play considerably. Also doing
this near the last move during selection doesn't affect speed much
but deteriorated play! Doing this first near the last move and then
look for tactics over the whole board as a next step affected
results negatively even more. Number of playouts are still in the
same ball-park.
Thinking it over, since I don't use this to prune the selection but
just to order the candidates I could see that after many runs the
ordering suggested by the tactics get overriden by the UCT
selection. So I could see the effect of using this for selection
reduced steadily with the number of runs through a node. But still
I didn't expect a considerable reduction in strength. So what could
be happening here?
- I could have a bug.
- I didn't run enough games (about 50)
- Using knowledge to order the initial selection is counter-
productive when not accompanied with pruning.
The last one I find very hard to believe. Did anyone else run into
something like this?
Finally, I also looked a bit at using more threads to make use of
more than one processor. I figure this can wait and it's better to
keep things simple at this early stage but still it's something I
want to keep in mind. When looking at what I need to do to enable
multiple threads during search it seems to me I'll be required to
lock substantial parts of the UCT-tree. This means traversing the
tree when looking for the best node to expand is going to be the
main bottle-neck. Maybe not with just two to four processors, but I
foresee substantial diminishing returns after that. Is this
correct? Is there experience with many processors? Maybe a
different expansion algorithm will be required?
Mark
_______________________________________________
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/
_______________________________________________
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/
