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## [Bug-gnubg] Neural network symmetry question

 From: Mark Higgins Subject: [Bug-gnubg] Neural network symmetry question Date: Fri, 9 Dec 2011 22:17:05 -0500

```I've been playing around a bit with neural networks for backgammon and found
something interesting, and want to see whether this is already part of gnubg.

Assume a Tesauro-style network with the usual inputs, and some number of hidden
nodes. And for simplicity, just one output representing the probability of win.

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).

But that constraint isn't satisfied with the usual TD setup.

If however you make a few assumptions:

* Hidden layer nodes don't include bias weight.
* Hidden->input weights have a specific symmetry: weight of the i'th hidden
node vs the j'th input node = w(i,j) = -w(i,j*), where j* is the index of the
other player's corresponding position.
* Output layer node doesn't include a bias weight.

Then you can show that, for each set of output->hidden node weights, those
weights sum to zero, the flip-the-perspective constraint is satisfied.

This seems to reduce the number of weights by about half, since you need only
half the middle weights. The network should be more accurate since a known
symmetry is respected, and should converge quicker since there are fewer
parameters to optimize.

You can generalize to a bias weight on the output node; in that case, the
constraint is on the bias weight that it = -1/2 sum( output->hidden node
weights ).

You can generalize as well to including a "gammon win" output node. In this
case there are no constraints on the output->hidden node weights, but the
probability of a gammon loss can be calculated from the probability of a gammon
win weights, and you don't need to explicitly include an output node for the
gammon loss.

I googled around a fair bit but couldn't figure out whether this is well known
or already included somewhere in gnubg. I took a look through eval.c but it's a
bit daunting. :) Is there documentation somewhere that I've just not found?

```