OPRIDE improves query efficiency in offline PbRL via a principled in-dataset exploration strategy and discount scheduling, outperforming prior methods with fewer queries and providing theoretical guarantees.
D., and Brown, D
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
SPLC uses social preference feedback to auto-generate preference data for offline RL, improving socially compliant crowd robot navigation over baselines.
citing papers explorer
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OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration
OPRIDE improves query efficiency in offline PbRL via a principled in-dataset exploration strategy and discount scheduling, outperforming prior methods with fewer queries and providing theoretical guarantees.
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SPLC: Social Preference Learning for Crowd Robot Navigation
SPLC uses social preference feedback to auto-generate preference data for offline RL, improving socially compliant crowd robot navigation over baselines.