Diversity-Aware Experience Replay via Statistical Leverage Scores

The paper proposes scoring each transition in the replay buffer with a statistical leverage score and drawing mini-batches from a mixture of this score and uniform sampling, to alleviate the under-sampling of rare events in robot offline experience. When actions are stored as one-hot encodings, the method instead uses the feature column with the largest kurtosis, and it drops the importance-weight correction of prioritized replay, directly optimizing a rebalanced objective; for rare cases related to the target, it applies the same tail rule to short trajectory windows.