6 citations · 23 across the 31 of their papers we have counts for
16 papers · 1 filter
LASER: Learning Active Sensing for Continuum Field Reconstruction
Huayu Deng, Jinghui Zhong, Xiangming Zhu +2
High-fidelity measurements of continuum physical fields are essential for scientific discovery and engineering design but remain challenging under sparse and constrained sensing. C…
Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
Haochen Yuan, Yichen Song, Yunbo Wang +1
Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, whi…
Goal-Driven Reward by Video Diffusion Models for Reinforcement Learning
Qi Wang, Mian Wu, Yuyang Zhang +7
Reinforcement Learning (RL) has achieved remarkable success in various domains, yet it often relies on carefully designed programmatic reward functions to guide agent behavior. Des…
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization
Haochen Yuan, Minting Pan, Yunbo Wang +3
Reinforcement learning (RL) has shown significant promise for sequential portfolio optimization tasks, such as stock trading, where the objective is to maximize cumulative returns…
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach
Minting Pan, Yitao Zheng, Jiajian Li +2
Offline reinforcement learning (RL) enables policy optimization using static datasets, avoiding the risks and costs of extensive real-world exploration. However, it struggles with…
Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning
Mingqi Yuan, Qi Wang, Guozheng Ma +8
Developing lifelong learning agents is crucial for artificial general intelligence (AGI). However, deep reinforcement learning (RL) systems often suffer from plasticity loss, where…
DimINO: Dimension-Informed Neural Operator Learning
Yichen Song, Yalun Wu, Yunbo Wang +1
In computational physics, a longstanding challenge lies in finding numerical solutions to partial differential equations (PDEs). Recently, research attention has increasingly focus…
EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
Huayu Deng, Xiangming Zhu, Yunbo Wang +1
Graph neural networks have been a powerful tool for mesh-based physical simulation. To efficiently model large-scale systems, existing methods mainly employ hierarchical graph stru…
Open-World Reinforcement Learning over Long Short-Term Imagination
Jiajian Li, Qi Wang, Yunbo Wang +4
Training visual reinforcement learning agents in a high-dimensional open world presents significant challenges. While various model-based methods have improved sample efficiency by…
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
Haochen Yuan, Yutong Wang, Yihong Chen +2
Time series forecasting, particularly in few-shot learning scenarios, is challenging due to the limited availability of high-quality training data. To address this, we present a pi…
Model-Based Reinforcement Learning with Multi-Task Offline Pretraining
Minting Pan, Yitao Zheng, Yunbo Wang +1
Pretraining reinforcement learning (RL) models on offline datasets is a promising way to improve their training efficiency in online tasks, but challenging due to the inherent mism…
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning
Qi Wang, Junming Yang, Yunbo Wang +3
Training offline RL models using visual inputs poses two significant challenges, i.e., the overfitting problem in representation learning and the overestimation bias for expected f…
Model-Based Reinforcement Learning with Isolated Imaginations
Minting Pan, Xiangming Zhu, Yitao Zheng +2
World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are indep…
Continual Visual Reinforcement Learning with A Life-Long World Model
Minting Pan, Wendong Zhang, Geng Chen +4
Learning physical dynamics in a series of non-stationary environments is a challenging but essential task for model-based reinforcement learning (MBRL) with visual inputs. It requi…
Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models
Minting Pan, Xiangming Zhu, Yunbo Wang +1
World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios such as autonomous driving, there commonly exists noncontrollabl…
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
Shanyan Guan, Huayu Deng, Yunbo Wang +1
Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids. Existing approaches, however, require the supervision of cons…