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Scale exploration with Gini impurity of policy values (#44)
First, when computing policy values in a position, we also calculate the Gini impurity, defined as (1 - sum of squares of policy values). A high Gini impurity indicates that there are many strong candidate moves in a position, and vice versa. This Gini impurity is then used to adjust the exploration scaling using a logarithmic formula. For higher values of Gini impurity, we decrease the exploration value so that the search focuses more on exploring variations with high q values. Conversely, for positions with low Gini impurity where one move is much better than the others, we increase the exploration value to ensure that other potential lines are not prematurely discarded. The idea to use Gini impurity was first proposed and tested by @Viren6. This patch was shown to affect the quality of data produced, so it has been intentionally excluded for datagen. Passed STC: https://montychess.org/tests/view/66aef6280f6f1e65cfa2b1f8 LLR: 2.93 (-2.94,2.94) <0.00,4.00> Total: 6944 W: 1626 L: 1460 D: 3858 Ptnml(0-2): 54, 787, 1643, 915, 73 Passed LTC: https://montychess.org/tests/view/66af1fd90f6f1e65cfa2b235 LLR: 2.93 (-2.94,2.94) <1.00,5.00> Total: 8718 W: 1877 L: 1705 D: 5136 Ptnml(0-2): 40, 931, 2255, 1083, 50 Rebased STC: https://montychess.org/tests/view/66b053380f6f1e65cfa2b657 LLR: 2.91 (-2.94,2.94) <0.00,4.00> Total: 9216 W: 2083 L: 1913 D: 5220 Ptnml(0-2): 82, 998, 2291, 1142, 95 2nd Rebased STC: https://montychess.org/tests/view/66bd91bf68e8f7e2fe23cfde LLR: 3.03 (-2.94,2.94) <0.00,4.00> Total: 4448 W: 1158 L: 982 D: 2308 Ptnml(0-2): 55, 487, 997, 597, 88 Bench: 1317423
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