7 papers · 1 filter
BRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Models
Shaowei Zhang, Jiahan Cao, Xunlan Zhou +2
Model-based Reinforcement Learning (MBRL) has achieved remarkable success in continuous control by leveraging latent world models. However, prevailing approaches typically rely on…
Co-Evolving Latent Action World Models
Yucen Wang, Fengming Zhang, De-Chuan Zhan +3
Adapting pretrained video generation models into controllable world models via latent actions is a promising step towards creating generalist world models. The dominant paradigm ad…
Reward Models in Deep Reinforcement Learning: A Survey
Rui Yu, Shenghua Wan, Yucen Wang +4
In reinforcement learning (RL), agents continually interact with the environment and use the feedback to refine their behavior. To guide policy optimization, reward models are intr…
DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning
Hanye Zhao, Xiaoshen Han, Zhengbang Zhu +4
With the great success of diffusion models (DMs) in generating realistic synthetic vision data, many researchers have investigated their potential in decision-making and control. M…
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch
Wen-Shu Fan, Xin-Chun Li, De-Chuan Zhan
Knowledge Distillation (KD) could transfer the ``dark knowledge" of a well-performed yet large neural network to a weaker but lightweight one. From the view of output logits and so…
DIDA: Denoised Imitation Learning based on Domain Adaptation
Kaichen Huang, Hai-Hang Sun, Shenghua Wan +4
Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications. In this work, we focus on th…
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors
Yucen Wang, Shenghua Wan, Le Gan +2
Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous…