Seems related to “temporal difference learning.” Each position’s max value should equal the next position’s min value in the asymptote, so you train the system accordingly.
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Xavier Combelle Sent: Saturday, January 30, 2016 11:08 AM To: computer-go@computer-go.org Subject: [Computer-go] A proposition to improve neural network based on min max I had got an idea but I don't think I'm strong enough programmer to implement it. (In particular I know quite nothing about neural network) So I submit it here. If we have a neural network which is able to evaluate all the positions of a board. The following might help to improve it. >From a position given: check the max value of all the evaluations go to the next level in the tree check the min value of all the evaluations if the min value < max value train the network at the root level to target max value for the original position else go to next level and continue The reason why I think it could help is because the evaluation at a deeper level should be easier and so better than at a less deep level. Please give all your feedback on this idea (even: it's a stupid idea/you should implement it are welcome)
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