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SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning

ICLR 2022 · Reinforcement learning › RL from feedback · approach: Reward modeling versus direct optimization · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add SURF reward learning, including pseudo-labeling and temporal croppingPEBBLE · Meta-world manipulation taskssuccess rate: ~50% -> ~100% success rate on Window Open with 400 queriesHelped
Add SURF reward learningPEBBLE · DMControl locomotion tasksground-truth returnHelped
Add SURF reward learningPEBBLE with DrQ-v2 backbone · DMControl visual control tasksground-truth returnHelped
Add pseudo-labeling on confident unlabeled samples (ablation)PEBBLE reward learning · Walker-walk, 100 queriesground-truth returnHelped
Add temporal cropping to supervised reward learning (ablation)PEBBLE reward learning · Walker-walk, 100 queriesground-truth returnHelped
Add temporal cropping to semi-supervised reward learning (ablation)PEBBLE with pseudo-labeling · Walker-walk, 100 queriesground-truth returnHelped
Replace no augmentation with random amplitude scaling (RAS) (ablation)PEBBLE reward learning · Walker-walk, 100 queriesground-truth returnHelped
Replace no augmentation with Gaussian noise (GN) (ablation)PEBBLE reward learning · Walker-walk, 100 queriesground-truth returnHurt
Use temporal cropping instead of random amplitude scaling or Gaussian noise (ablation)PEBBLE reward learning · Walker-walk, 100 queriesground-truth returnHelped
Combine temporal cropping with random amplitude scaling (ablation)PEBBLE reward learning · Walker-walk, 100 queriesground-truth returnNo clear effect
Combine temporal cropping with Gaussian noise (ablation)PEBBLE reward learning · Walker-walk, 100 queriesground-truth returnNo clear effect
Increase the unlabeled batch ratio to 7 (ablation)SURF · Walker-walk, 100 queriesground-truth returnHurt

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