arXiv AI August 13, 2026 By Jordan Coblin, Han Wang, Martha White, Adam White

Dynamics Models for Offline Hyperparameter Selection in Real-World RL

arXiv:2608. 11349v1 Announce Type: cross Abstract: A key obstacle to deploying reinforcement learning in real-world systems is hyperparameter selection, particularly when simulators are unavailable and online experimentation is costly.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 17

Fully Offline Reinforcement Learning

arXiv:2505. 22442v3 Announce Type: replace-cross Abstract: Offline RL (ORL) promises safe and sample-efficient deployment but existing methods rely on undocumented online interactions for hyperparameter tuning and lack reliable fully offline estimates of initial online performance.

By Mattie Fellows, Clarisse Wibault, Uljad Berdica, Johannes Forkel, Maike Osborne, Jakob N. Foerster

reinforcement-learning

arXiv AI

Jul 14

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.

By Alper Kamil Bozkurt, Shangtong Zhang, Yuichi Motai

reinforcement-learning fine-tuning

arXiv AI

Jul 16

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners

arXiv:2607. 13274v1 Announce Type: cross Abstract: Reinforcement learning is increasingly being considered for controlling real-world systems, from fusion plasma and autonomous vehicles to drug discovery and drinking water treatment, where reliability is essential and tuning budgets are limited.

By Haseeb Shah, Lingwei Zhu, Adam White, Martha White

agents reinforcement-learning

arXiv Machine Learning

Sep 22

OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling

arXiv:2609.23753v1 Announce Type: cross Abstract: Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeli...

By Yikun Miao, Fangqi Zhu, Quanxin Shou, Xiaoyi Pang, Zhengyang Yan, Junhao Li, Haodong Wang, Zicong Hong, Song Guo

fine-tuning

arXiv AI

Jun 24

Offline Reinforcement Learning for Warehouse SLAM Throughput Control

arXiv:2606. 23978v1 Announce Type: cross Abstract: We present an offline reinforcement learning (RL) framework for optimizing SLAM throughput control in a warehouse fulfillment environment.

By Tina Dongxu Li, Mouhacine Benosman, Rajat Kumar, Kevin Tan, Ken Meszaros, Trevor Dardik

reinforcement-learning efficiency benchmarks

arXiv Machine Learning

2d ago

In-Distribution Imagination for Model-Based Offline Reinforcement Learning

arXiv:2609.38673v1 Announce Type: new Abstract: Model-based offline reinforcement learning (MBORL) improves sample efficiency through model-generated trajectories. However, accumulative model error c...

By Mintae Kim, Koushil Sreenath

reinforcement-learning

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Related stories

Fully Offline Reinforcement Learning

Active Offline-to-Online Reinforcement Learning

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners

OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling

Offline Reinforcement Learning for Warehouse SLAM Throughput Control

In-Distribution Imagination for Model-Based Offline Reinforcement Learning