arXiv Machine Learning July 27, 2026 By Timothy Tomashevskiy

Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 21646v1 Announce Type: new Abstract: Ensuring safety in reinforcement learning under nonstationarity requires determining whether a learning system can safely adapt to forecasted environmental change within the required recovery horizon.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 26

Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy

arXiv:2606. 26527v1 Announce Type: new Abstract: Transfer learning improves policy learning efficiency by reusing knowledge from source tasks, providing a feasible paradigm for safe and efficient autonomous highway lane changing decision-making.

By Wenjie Huang, Yang Li, Jingjia Teng, Mingwei Jin, Kai Song, Yougang Bian, Yongfu Li, Qisong Yang, Helai Huang

agents reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jul 23

Safety-Regulated Transfer Reinforcement Learning with Adaptive Teacher Guidance

arXiv:2606. 26527v2 Announce Type: replace Abstract: We propose Safety-Regulated Adaptive Transfer Reinforcement Learning (SRATRL), a teacher--student framework that combines safety-triggered intervention, safety-adaptive value shaping, and policy-compatibility-based optimization for efficient target-domain adaptation.

By Wenjie Huang, Yang Li, Jingjia Teng, Mingwei Jin, Kai Song, Zeyu Yang, Qisong Yang, Yougang Bian

reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jul 27

Safe In-Context Reinforcement Learning

arXiv:2509. 25582v4 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, instead relying on an expanding context of interaction history.

By Amir Moeini, Minjae Kwon, Alper Kamil Bozkurt, Yuichi Motai, Rohan Chandra, Lu Feng, Shangtong Zhang

agents reinforcement-learning benchmarks safety

arXiv AI

Aug 12

Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning

arXiv:2608. 10403v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown promising performance in autonomous driving, yet ensuring the safety of online RL policies remains challenging due to insufficient exposure to safety-critical driving scenes.

By Xincong Hu (Nanjing University), Lei Ou (Nanjing University), Maosen Li (Yinwang Intelligent Technology Co., Ltd), Jingtao Zhang (Yinwang Intelligent Technology Co., Ltd), Liguo Hou (Yinwang Intelligent Technology Co., Ltd), Zongzhang Zhang (Nanjing University)

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jun 25

Safe Learning Control with Optimality and Stability Guarantees

arXiv:2501. 15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance.

By Xinyang Wang, Hongwei Zhang, Shimin Wang, Wei Xiao, Martin Guay

reinforcement-learning robotics safety

arXiv Machine Learning

Jul 1

Safe Online Learning via Smooth Safety-Structured Policy Composition

arXiv:2606. 31320v1 Announce Type: new Abstract: Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics.

By Hongpeng Cao, Liqun Zhao, Yuliang Gu, Naira Hovakimyan, Lui Sha, Marco Caccamo

reinforcement-learning benchmarks safety

Related stories

Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy

Safety-Regulated Transfer Reinforcement Learning with Adaptive Teacher Guidance

Safe In-Context Reinforcement Learning

Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning

Safe Learning Control with Optimality and Stability Guarantees

Safe Online Learning via Smooth Safety-Structured Policy Composition