:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Wan, Xu, Yang, Chao, Yang, Cheng, Song, Jie, Sun, Mingyang
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2602.20729
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning
di: Wan, Xu, et al.
Pubblicazione: (2025)

LLM-Guided Safe Reinforcement Learning for Energy System Topology Reconfiguration
di: Zhang, Zongyan, et al.
Pubblicazione: (2026)

Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
di: Yang, Rui, et al.
Pubblicazione: (2024)

How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
di: Cheng, Xiaoyuan, et al.
Pubblicazione: (2026)

Reinforcement Learning by Guided Safe Exploration
di: Yang, Qisong, et al.
Pubblicazione: (2023)

A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
di: Guo, Siyuan, et al.
Pubblicazione: (2023)

FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning
di: Fu, Yuwei, et al.
Pubblicazione: (2024)

TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning
di: Wu, Jinyang, et al.
Pubblicazione: (2025)

UFO-RL: Uncertainty-Focused Optimization for Efficient Reinforcement Learning Data Selection
di: Zhao, Yang, et al.
Pubblicazione: (2025)

Conditional Sequence Modeling for Safe Reinforcement Learning
di: Bai, Wensong, et al.
Pubblicazione: (2026)

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

Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
di: Zheng, Yinan, et al.
Pubblicazione: (2024)

PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning
di: Huang, Dongchi, et al.
Pubblicazione: (2025)

SAMG: Offline-to-Online Reinforcement Learning via State-Action-Conditional Offline Model Guidance
di: Zhang, Liyu, et al.
Pubblicazione: (2024)

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment
di: Yang, Suorong, et al.
Pubblicazione: (2025)

AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models
di: Lu, Chengxuan, et al.
Pubblicazione: (2026)

Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
di: Wen, Xiaoyu, et al.
Pubblicazione: (2023)

ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning
di: Liu, Zeyuan, et al.
Pubblicazione: (2025)

Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
di: Günster, Jonas, et al.
Pubblicazione: (2024)

AdapThink: Adaptive Thinking Preferences for Reasoning Language Model
di: Wan, Xu, et al.
Pubblicazione: (2025)

Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
di: Yang, Zixuan, et al.
Pubblicazione: (2024)

Safe and Robust Reinforcement Learning: Principles and Practice
di: Yamagata, Taku, et al.
Pubblicazione: (2024)

OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
di: Luo, Yu, et al.
Pubblicazione: (2024)

Robust Shielding for Safe Reinforcement Learning
di: Court, Edwin Hamel-De le, et al.
Pubblicazione: (2026)

GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
di: Wang, Hanrui, et al.
Pubblicazione: (2020)

Feasible Policy Iteration for Safe Reinforcement Learning
di: Yang, Yujie, et al.
Pubblicazione: (2023)

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation
di: Yang, Wenjie, et al.
Pubblicazione: (2025)

Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertainty
di: Eisele, Artur, et al.
Pubblicazione: (2026)

Safe, Efficient, and Robust Reinforcement Learning for Ranking and Diffusion Models
di: Gupta, Shashank
Pubblicazione: (2025)

Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Making
di: Wan, Xu, et al.
Pubblicazione: (2025)

Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
di: Yang, Rui, et al.
Pubblicazione: (2023)

Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals
di: Wang, Vivienne Huiling, et al.
Pubblicazione: (2025)

Robust Probabilistic Shielding for Safe Offline Reinforcement Learning
di: Galesloot, Maris F. L., et al.
Pubblicazione: (2026)

ViSaRL: Visual Reinforcement Learning Guided by Human Saliency
di: Liang, Anthony, et al.
Pubblicazione: (2024)

Reinforce-Ada: An Adaptive Sampling Framework under Non-linear RL Objectives
di: Xiong, Wei, et al.
Pubblicazione: (2025)

Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
di: Ji, Jiaming, et al.
Pubblicazione: (2023)

UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning
di: Wu, Jiaxi, et al.
Pubblicazione: (2025)

ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment
di: Li, Xiuyu, et al.
Pubblicazione: (2026)

Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving
di: Wei, Qi, et al.
Pubblicazione: (2026)

CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs
di: Yao, Zhiyuan, et al.
Pubblicazione: (2026)