most citedFrom Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20261 cited

From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training

Jinwen Wang, Youfang Lin, Xiaobo Hu +4

Unsupervised pre-training on large-scale datasets has demonstrated significant potential for improving the sample efficiency and performance of Reinforcement Learning (RL). Given t…

cs.LG2026

Local Motion Matters: A Deconstruct-Recompose Paradigm for Reinforcement Learning Pre-training from Videos

Jinwen Wang, Youfang Lin, Xiaobo Hu +2

Pre-training on large-scale videos to improve reinforcement learning efficiency is promising yet remains challenging. Existing methods typically treat the agent as an indivisible e…

cs.LG2026

Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization

Jinwen Wang, Youfang Lin, Xiaobo Hu +4

Visual Reinforcement Learning (VRL) has achieved considerable success in solving control tasks. However, generalizing learned policies to new environments remains a major challenge…

cs.CV2025

Learning Robust Representations via Bidirectional Transition for Visual Reinforcement Learning

Xiaobo Hu, Youfang Lin, Yue Liu +4

Visual reinforcement learning has proven effective in solving control tasks with high-dimensional observations. However, extracting reliable and generalizable representations from…

cs.MA2025

CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness

Shoucheng Song, Youfang Lin, Sheng Han +4

Communication has been widely employed to enhance multi-agent collaboration. Previous research has typically assumed delay-free communication, a strong assumption that is challengi…