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.
Offline meta reinforcement learning–identifiability challenges and effective data collection strategies.Advances in Neural Information Processing Systems, 34:4607–4618, 2021
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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.