UBP2 uses ensembles of reward, dynamics, and value models to score trajectories on a unified objective of reward plus uncertainty, yielding sublinear regret bounds and higher sample efficiency on Meta-World than prior preference-based methods.
Reward uncertainty for exploration in preference-based reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
citing papers explorer
-
UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning
UBP2 uses ensembles of reward, dynamics, and value models to score trajectories on a unified objective of reward plus uncertainty, yielding sublinear regret bounds and higher sample efficiency on Meta-World than prior preference-based methods.
-
SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.