most citedTrain Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning

1 citations · 1 across the 1 of their papers we have counts for

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cs.LG2023★ 1 cited

Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning

Shenzhi Wang, Qisen Yang, Jiawei Gao +6

Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-training on a pre-collected dataset with fine-tuning in an online environment. However, the i…