Diversity-Aware Experience Replay via Statistical Leverage Scores
Overview
This research introduces a diversity-aware experience replay method using statistical leverage scores to improve off-policy deep reinforcement learning in physical robots. The technique identifies rare and valuable transitions by analyzing feature column kurtosis and trajectory windows relative to goals, eliminating the need for labels or value networks. On a three-joint arm task requiring an object to be pushed around an obstacle, the approach raised success rates from 0.49 to 0.69 against an oracle ceiling of 0.74, effectively addressing the issue where replay buffers are dominated by routine data.
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Reported quantity · success rate: 0.49 other · reported trials
“n the rare situations of a log are relational, visible only in how a stretch of behavior relates to its goal, not in any single stored coordinate-per-transition scores are blind; applying the same tail rule to short trajectory windows described relative to their goal restores the signal, and on a three-joint arm that must push an object around an obstacle it raises the rare-task success from 0.49 to 0.69 against a la”
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- arXiv Robotics — research abstractsSignalDiversity-Aware Experience Replay via Statistical Leverage Scores
This research introduces a diversity-aware experience replay method using statistical leverage scores to improve off-policy deep reinforcement learning in physical robots. The technique identifies rare and valuable transitions by analyzing feature column kurtosis and trajectory windows relative to goals, eliminating the need for labels or value networks. On a three-joint arm task requiring an object to be pushed around an obstacle, the approach raised success rates from 0.49 to 0.69 against an oracle ceiling of 0.74, effectively addressing the issue where replay buffers are dominated by routine data.
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Current attention 3·Peak within the comparable range 3（2026-11-06 22:00 UTC）·Change within the comparable range over 24 hours –
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