Technology
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning Qi Wang 1, 2 Junming Y ang 3 Y unbo Wang
Training offline RL models using visual inputs poses two significant challenges, i.e.,
Iterative Reachability Estimation for Safe Reinforcement Learning
We theoretically establish that our algorithms almost surely converge to locally optimal policies of our safe optimization framework.
SfPUEL: Shape from Polarization under Unknown Environment Light
DeepSfP (4), which is even comparable with the multiview SfP method P ANDORA (15). In addition, metallic and dielectric surfaces exhibit different polarization BRDFs under the same illumination, which causes AoLP maps to vary on different materials, further compounding the normal estimation problem.
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling
From left to right: a) renderings, b) input images, c) normals.
A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
Such causal graphs delineate the relations among alarms and can significantly aid engineers in identifying and rectifying faults. However, existing methods either ignore the topological relationships among devices or suffer from relatively low scalability and efficiency, failing to deliver high-quality responses in a timely manner.