6 citations · 9 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
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…
PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting
Yuxin Jia, Youfang Lin, Jing Yu +3
Due to the recurrent structure of RNN, the long information propagation path poses limitations in capturing long-term dependencies, gradient explosion/vanishing issues, and ineffic…
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space
Xiaoyang Yu, Youfang Lin, Shuo Wang +2
In a multi-agent system (MAS), action semantics indicates the different influences of agents' actions toward other entities, and can be used to divide agents into groups in a physi…
Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation
Xiaobo Hu, Youfang Lin, HeHe Fan +3
Given an object of interest, visual navigation aims to reach the object's location based on a sequence of partial observations. To this end, an agent needs to 1) learn a piece of c…
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…