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Found 3 papers, 2 papers with code
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Existing solutions tackle this problem by imposing a policy constraint on the policy improvement objective in both offline and online learning.
Boosting Offline Reinforcement Learning with Action Preference Query
In particular, online fine-tuning has become a commonly used method to correct the erroneous estimates of out-of-distribution data learned in the offline training phase.
A Mixture of Surprises for Unsupervised Reinforcement Learning
However, both strategies rely on a strong assumption: the entropy of the environment's dynamics is either high or low.
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