世界模型

Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models

通过基于恢复的防护机制实现安全强化学习

Alexander W. Goodall, Francesco Belardinelli

机构 * Imperial College London, Department of Computing（伦敦帝国学院计算机系） 纠错

专题命中 模型式强化学习 ：dynamics model(title)；分类 cs.AI、cs.LG

AI总结 本文提出一种基于高斯过程的恢复防护机制，用于在未知非线性系统中实现安全强化学习，通过动态恢复和内部模型采样实现安全与高效的学习。

Comments Accepted at AAMAS 2026

Uni-Flow: a unified autoregressive-diffusion model for complex multiscale flows

Uni-Flow：一种统一的自回归-扩散模型用于复杂多尺度流体

Xiao Xue, Tianyue Yang, Mingyang Gao, Leyu Pan, Maida Wang, Kewei Zhu, Shuo Wang, Jiuling Li, Marco F. P. ten Eikelder, Peter V. Coveney

机构 * Centre for Computational Science, University College London, London, UK ； Department of Earth Science ； Engineering, Imperial College London, London, UK ； Department of Chemical Engineering, University College London, London, UK ； Department of Physics, Eindhoven University of Technology, Eindhoven, Netherlands ； School of Civil \& Environmental Engineering, Queensland University of Technology, Brisbane, Australia ； Australian Centre for Water ； Environmental Biotechnology, The University of Queensland, Brisbane, Australia ； Institute for Mechanics, Computational Mechanics Group, Technical University of Darmstadt, Germany ； Centre for Advanced Research Computing, University College London, London, UK 纠错

专题命中 模型式强化学习 ：latent dynamics(abstract)；分类 cs.LG

AI总结 Uni-Flow通过统一自回归-扩散模型，实现复杂多尺度流体的高效建模与高分辨率重构。