Hugging Face Trending Papers
Safe Meta-Policy Design with Risk Control
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arXiv Machine Learning
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Safe Meta-Policy Design with Risk Control
arXiv:2610.10393v1 Announce Type: cross Abstract: Models can be retrained as new data arrive, but deploying every new version risks replacing a good policy with a worse one. We study how to plan poli...
arXiv AI
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ProSpec RL: Plan Ahead, then Execute
arXiv:2407. 21359v2 Announce Type: replace-cross Abstract: Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition.
arXiv AI
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Active Offline-to-Online Reinforcement Learning
arXiv:2607. 11720v1 Announce Type: cross Abstract: Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction.
Hugging Face Trending Papers
Jul 13
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.
arXiv Machine Learning
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Are Good Generators Good Decision-Makers? Policy Learning for General Interventions via Retargeted Counterfactual Generation
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arXiv Machine Learning
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Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
arXiv:2605. 26919v2 Announce Type: replace Abstract: Maintaining predictive accuracy in non-stationary environments requires online model selection to adapt autonomously to unknown distribution shifts.