5 citations · 8 across the 8 of their papers we have counts for
8 papers
Continual Task Learning through Adaptive Policy Self-Composition
Shengchao Hu, Yuhang Zhou, Ziqing Fan +4
Training a generalizable agent to continually learn a sequence of tasks from offline trajectories is a natural requirement for long-lived agents, yet remains a significant challeng…
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
Ziqing Fan, Shengchao Hu, Yuhang Zhou +4
The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy applicable to diverse tasks without the need for online environmental interaction. Re…
Communication Learning in Multi-Agent Systems from Graph Modeling Perspective
Shengchao Hu, Li Shen, Ya Zhang +1
In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives. To enh…
Reconstruct the Pruned Model without Any Retraining
Pingjie Wang, Ziqing Fan, Shengchao Hu +3
Structured pruning is a promising hardware-friendly compression technique for large language models (LLMs), which is expected to be retraining-free to avoid the enormous retraining…
Prompt-Tuning Decision Transformer with Preference Ranking
Shengchao Hu, Li Shen, Ya Zhang +1
Prompt-tuning has emerged as a promising method for adapting pre-trained models to downstream tasks or aligning with human preferences. Prompt learning is widely used in NLP but ha…
Graph Decision Transformer
Shengchao Hu, Li Shen, Ya Zhang +1
Offline reinforcement learning (RL) is a challenging task, whose objective is to learn policies from static trajectory data without interacting with the environment. Recently, offl…