[Computer-go] MCTS + Neural Networks?

Steven Clark steven.p.clark at gmail.com
Wed May 1 19:10:16 PDT 2013

Thanks for the link! Looks like a good paper -- I will read it more
carefully shortly.
Ignoring computational speed for a moment, is it a reasonable assumption
that an algorithm that plays a NN-proposed tactical move 50% of the time,
and a random move 50% of the time, should outperform an algorithm that
plays a random move 100% of the time?
So it's just a case of how many playouts do we lose by employing the NN
(GPUs to the rescue?). For reference, I was using 25 input nodes, 25 output
nodes, ~50 hidden nodes.
I guess ultimately it comes down to "make a bot and prove it" :)


On Wed, May 1, 2013 at 9:50 PM, George Dahl <gdahl at cs.toronto.edu> wrote:

> I don't know if neural nets that predict moves have been helpful in any
> strong bots, but predicting expert moves with neural nets is certainly old
> news. See http://www.cs.utoronto.ca/~ilya/pubs/2008/go_paper.pdf
> There might be a place for artificial neural nets in a strong Go playing
> program, but it is an open question on how to incorporate neural nets well.
> Software like neurgo used a lot of expert features along with a neural net
> for global position evaluation and I tried (with very little success) to
> predict ownership of points on the board using a neural net.
> It is very hard to get neural nets to help a standard MCTS bot a lot
> because the neural net needs to be good at whatever it is supposed to be
> doing and still probably very fast to be useful.
> - George
> On Wed, May 1, 2013 at 9:42 PM, Steven Clark <steven.p.clark at gmail.com>wrote:
>> Hello all-
>> Has anyone successfully used neural nets to help guide MC playouts?
>> Has anyone used NN to learn patterns larger than 3x3?
>> I'm working on a grad-school project, and discovered a few interesting
>> things.
>> After analyzing 10,000+ high-dan games from KGS, I find that more than
>> 50% of the time, moves are played within a 5x5 window centered at the
>> opponent's previous move (call this a "tactical" move, vs a strategic move).
>> I used the FANN library to learn these 5x5 patterns, and found that the
>> NN could predict tactical moves with ~27% accuracy (and with a >50% chance
>> that the answer would be in the top 3 moves proposed by the NN).
>> Is this old news? Are neural nets just too slow to be helpful to MC
>> (reduce the playout rate too much?)
>> Thoughts welcome. I will be up late finishing the report since it is due
>> tomorrow ;)
>> -Steven
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