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🔗 Source: proceedings.mlr.press
📊 Size: 970 KB
📄 Pages: 11 pages
⬇️ Downloads: 296

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Recent advances in deep offline reinforcement learning (RL) enable training strong robotic agents from offline datasets, but fine-tuning these agents via online interactions can be challenging due to state-action distribution shift, leading to severe bootstrap error. To address this, a balanced replay scheme is proposed, prioritizing online samples while encouraging the use of near-on-policy samples from the offline dataset. Additionally, a pessimistic Q-ensemble scheme leverages multiple Q-functions trained pessimistically offline to prevent overoptimism about unfamiliar actions at novel states. The proposed method improves sample efficiency and final performance of fine-tuned robotic agents on various locomotion and manipulation tasks.

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Recent advances in deep offline reinforcement learning (RL) enable training strong robotic agents from offline datasets, but fine-tuning these agents via...

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  • File Format: PDF
  • File Size: 970 KB
  • Pages: 11
  • Language: EN
  • Total Downloads: 296
  • Last Updated: 6 days ago

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