Active Offline-to-Online Reinforcement Learning
Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction. This offline-to-online RL (O2O-RL) paradigm is particularly promising in nonstationary domains where interaction is costly or potentially hazardous. Standard O2O-RL pipelines train multiple candidate policies offline, evaluate them using off-policy or online evaluation, and then deploy and fine-tune the po
Record details
Published: 13 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation
Retrieved: 14 July 2026
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ethics.ai (13 July 2026), “Active Offline-to-Online Reinforcement Learning,” evidence record 3018, https://ethics.ai/record/3018 (originally published by arXiv cs.AI).
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