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 policy with the highest estimated value. However, as in offline pretraining, fine-tuning performance i
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 55%AgileRL/AgileRL →
- PossiblePossibly related (embedding) · 55%hscspring/rl-llm-nlp →
- PossiblePossibly related (embedding) · 50%redai-infra/Relax →
- PossiblePossibly related (embedding) · 49%RL without TD learning →
- PossiblePossibly related (embedding) · 47%Best practices for multi-turn reinforcement learning in Amazon SageMaker AI →
- LinkedLinked via arxiv author · 85%Alper Kamil Bozkurt →
“Active Offline-to-Online Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Shangtong Zhang →
“Active Offline-to-Online Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Yuichi Motai →
“Active Offline-to-Online Reinforcement Learning”