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Hey,
I implemented the IS weights based on my understanding. In$N$ and $P(i)$ according to the total number of samples in buffer and game/position sampling probabilities (with parameter
replay_buffer.py
, I calculatedself.PER_beta
). Then I normalize weights by 1/ maxi wi after getting theweight_batch
. During training, theweights_batch
is multiplied by the loss of each data sample.