Offline-to-online value adaptation in RL has a minimax lower bound matching pure online learning in hard cases, yet O2O-LSVI improves sample complexity under a novel structural condition on pretrained Q-functions.
arXiv preprint arXiv:2203.05804 , year=
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POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.
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Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
Offline-to-online value adaptation in RL has a minimax lower bound matching pure online learning in hard cases, yet O2O-LSVI improves sample complexity under a novel structural condition on pretrained Q-functions.
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Privacy Preserving Reinforcement Learning with One-Sided Feedback
POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.