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Very nice.
Now to wrap it up make your experiments scientifically sound. Run your experiments many times and average to make sure you get a representative number, then repeat for different values of "turns" to plot the behaviour. Does the learning increase? does it level off after some number of turns? Does too much learning have a negative effect (likely an error in your model) or do the networks stabilize... etc..
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Those are some good results.
But as perspective said, make sure to test your model extensively. I've seen many a learning agent "appear" to be working, but only because it was in the right circumstances.
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What is an optimal data structor for an ANN? Linked list tree (thaT seems most logical)?