Warm-start RL (WSRL) fine-tunes offline-pretrained RL agents online with no offline data retention, using 5,000 warm-up rollouts from the frozen pre-trained policy followed by standard high-UTD SAC.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data
Warm-start RL (WSRL) fine-tunes offline-pretrained RL agents online with no offline data retention, using 5,000 warm-up rollouts from the frozen pre-trained policy followed by standard high-UTD SAC.