Copertina

Learning When to Switch: Adaptive Policy Selection via Reinforcement Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Tava, Chris
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning
di: Chemingui, Yassine, et al.
Pubblicazione: (2024)

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
di: Liu, Vincent, et al.
Pubblicazione: (2023)

To Switch or Not to Switch? Balanced Policy Switching in Offline Reinforcement Learning
di: Ma, Tao, et al.
Pubblicazione: (2024)

Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
di: Ning, Alex, et al.
Pubblicazione: (2025)

Evaluation-Time Policy Switching for Offline Reinforcement Learning
di: Neggatu, Natinael Solomon, et al.
Pubblicazione: (2025)

Reinforcement Learning for Adaptive MCMC
di: Wang, Congye, et al.
Pubblicazione: (2024)

Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning
di: Ikponmwoba, Eloghosa, et al.
Pubblicazione: (2026)

Scalable On-Policy Reinforcement Learning via Adaptive Batch Scaling
di: Park, Jongchan
Pubblicazione: (2026)

Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning
di: Jing, Tan, et al.
Pubblicazione: (2025)

Adaptive Policy Synchronization for Scalable Reinforcement Learning
di: Lafuente-Mercado, Rodney
Pubblicazione: (2025)

Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
di: Bozkurt, Alper Kamil, et al.
Pubblicazione: (2026)

Adaptive Active Learning for Regression via Reinforcement Learning
di: Nguyen, Simon D., et al.
Pubblicazione: (2026)

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
di: Torres, José Eduardo Zerna, et al.
Pubblicazione: (2025)

Harnessing the Power of Reinforcement Learning for Adaptive MCMC
di: Wang, Congye, et al.
Pubblicazione: (2025)

Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
di: Silvestri, Gianluigi, et al.
Pubblicazione: (2024)

Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning
di: Illian, Marvin, et al.
Pubblicazione: (2026)

Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning
di: Dang, Haoran, et al.
Pubblicazione: (2026)

AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning
di: Yan, Renye, et al.
Pubblicazione: (2024)

Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts
di: Zheng, Haizhong, et al.
Pubblicazione: (2025)

Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
di: Bertsimas, Dimitris, et al.
Pubblicazione: (2024)

Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
di: Madhow, Sunil, et al.
Pubblicazione: (2023)

Policy Improvement Reinforcement Learning
di: Wang, Huaiyang, et al.
Pubblicazione: (2026)

Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
di: Haroon, Adam, et al.
Pubblicazione: (2026)

BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via Balanced Policy Optimization with Adaptive Clipping
di: Xi, Zhiheng, et al.
Pubblicazione: (2025)

Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
di: Liu, Tenglong, et al.
Pubblicazione: (2024)

A Reinforcement Learning Inspired Latent Yield Based Adaptive Algorithm Switching Mechanism
di: Nair, Jayprakash S., et al.
Pubblicazione: (2026)

Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning
di: Kar, Avik, et al.
Pubblicazione: (2024)

On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling
di: Corrado, Nicholas E., et al.
Pubblicazione: (2023)

Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning
di: Ayoub, Alex, et al.
Pubblicazione: (2024)

Switching Successor Measures for Hierarchical Zero-shot Reinforcement Learning
di: Stojanovic, Stefan, et al.
Pubblicazione: (2026)

When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?
di: Nisioti, Eleni, et al.
Pubblicazione: (2025)

Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction
di: Zuo, Shuozhi, et al.
Pubblicazione: (2026)

Pausing Policy Learning in Non-stationary Reinforcement Learning
di: Lee, Hyunin, et al.
Pubblicazione: (2024)

Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning
di: Vincent, Théo, et al.
Pubblicazione: (2024)

Reinforcement Learning for Flow-Matching Policies
di: Pfrommer, Samuel, et al.
Pubblicazione: (2025)

Policy Bifurcation in Safe Reinforcement Learning
di: Zou, Wenjun, et al.
Pubblicazione: (2024)

Explainable Reinforcement Learning via Temporal Policy Decomposition
di: Ruggeri, Franco, et al.
Pubblicazione: (2025)

Online Matching via Reinforcement Learning: An Expert Policy Orchestration Strategy
di: Mignacco, Chiara, et al.
Pubblicazione: (2025)

The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?
di: Digalakis Jr, Vassilis, et al.
Pubblicazione: (2025)

Reinforcement Learning with Curriculum-inspired Adaptive Direct Policy Guidance for Truck Dispatching
di: Meng, Shi, et al.
Pubblicazione: (2025)