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Prioritized Experience Replay (PER) is a deep reinforcement learningtechnique in which agents learn from transitions sampled with non-uniformprobability proportionate to their temporal-difference error. We show that anyloss function evaluated with non-uniformly sampled data can be transformed intoanother uniformly sampled loss function with the same expected gradient.Surprisingly, we find in some environments PER can be replaced entirely by thisnew loss function without impact to empirical performance. Furthermore, thisrelationship suggests a new branch of improvements to PER by correcting itsuniformly sampled loss function equivalent. We demonstrate the effectiveness ofour proposed modifications to PER and the equivalent loss function in severalMuJoCo and Atari environments.

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay