An adaptive UCB-based policy selection and fine-tuning strategy improves performance over standard O2O-RL baselines under interaction budgets.
Addressing function approximation error in actor-critic methods
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
OGPO enables sample-efficient full-finetuning of generative control policies via off-policy critics and modified PPO, achieving SOTA on robot manipulation tasks while rescuing poorly initialized behavior cloning policies without expert data.
citing papers explorer
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Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
An adaptive UCB-based policy selection and fine-tuning strategy improves performance over standard O2O-RL baselines under interaction budgets.
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OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
OGPO enables sample-efficient full-finetuning of generative control policies via off-policy critics and modified PPO, achieving SOTA on robot manipulation tasks while rescuing poorly initialized behavior cloning policies without expert data.