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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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.