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Re: [Bug-gnubg] Neural network symmetry question

From: Mark Higgins
Subject: Re: [Bug-gnubg] Neural network symmetry question
Date: Sun, 11 Dec 2011 17:22:45 -0500

I tried a little experiment on this: a 10-hidden-node network with a single 
probability-of-win output, but two setups. The first doesn't have a "whose turn 
is it" input and doesn't add any symmetry constraints. The second has the extra 
inputs for the turn and makes the symmetry constraint I described.

I trained them in parallel and benchmarked them against pub eval and against 
each other.

The symmetric case performed a little better: it trained more quickly, did 
better against pub eval, and was on par or a little better than the other case 
when playing head to head.

Details and data here:


Of course not conclusive with such a simple setup, but kind of suggestive 

On Dec 10, 2011, at 2:22 PM, Mark Higgins wrote:

> Thx! Makes sense. Though I wonder if adding back in the "whose move is it" 
> input and reducing the hidden->output weights by half ends up as a net 
> benefit for training. Maybe I'll test it out. 
> On Dec 10, 2011, at 2:06 PM, Frank Berger <address@hidden> wrote:
>> Hi Mark,
>>> If I take a given board and translate the position into the inputs and then 
>>> evaluate the network, it gives me a probability of win. If I then flip the 
>>> board's perspective (ie white vs black) and do the same, I get another 
>>> probability of win. Those two probabilities should sum to 1, since one or 
>>> the other player must win (or equivalently, the probability of white 
>>> winning = probability of black losing = 1 - probability of black winning).
>> I assume your assumption is wrong. IIRC in an earlier paper there was an 
>> input to indicate who's on. It is much simpler to present the position from 
>> the point of the moving player, because the net has to learn less. I'm not 
>> that familiar with the gnubg code, but I think they do it in this way, so 
>> you can't just turn the perspective.
>> ciao
>> Frank
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