Tactics in training data
Posted: Sat Jun 12, 2021 12:10 pm
Hi everyone.
I am currently trying to train a network for Loki that is actually able to play chess. One problem I have though, is that now that I use search results instead of static evaluations for the training points, the networks play completely idiotic moves (walking into mate, giving the queen away for free etc..). They are extremely bad compared to the ones trained on static evaluations, and I don't really know why.
Right now, I am trying to train a net with quiescence search scores, but this doesn't look too good either
One reason could be that the search results are heavily influenced by tactics which the nets aren't supposed to know anyway. Therefore, my first question is: Should I do anything to resolve the captures before getting a search score?
My second question concerns how to actually resolve the captures. Is that just done by using quiescence search to get a quiet position and then searching that to get a data point?
Thanks in advance
PS. My network's architecture is 768 neurons (12 pieces * 64 squares) input neurons, then three hidden layers with 256, 32 and 32 neurons in that order, and a single output neuron.
I am currently trying to train a network for Loki that is actually able to play chess. One problem I have though, is that now that I use search results instead of static evaluations for the training points, the networks play completely idiotic moves (walking into mate, giving the queen away for free etc..). They are extremely bad compared to the ones trained on static evaluations, and I don't really know why.
Right now, I am trying to train a net with quiescence search scores, but this doesn't look too good either
One reason could be that the search results are heavily influenced by tactics which the nets aren't supposed to know anyway. Therefore, my first question is: Should I do anything to resolve the captures before getting a search score?
My second question concerns how to actually resolve the captures. Is that just done by using quiescence search to get a quiet position and then searching that to get a data point?
Thanks in advance
PS. My network's architecture is 768 neurons (12 pieces * 64 squares) input neurons, then three hidden layers with 256, 32 and 32 neurons in that order, and a single output neuron.