5 citations · 16 across the 15 of their papers we have counts for
7 papers · 2 filters
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…
Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization
Shengchao Hu, Wanru Zhao, Weixiong Lin +3
Offline reinforcement learning (RL) methods harness previous experiences to derive an optimal policy, forming the foundation for pre-trained large-scale models (PLMs). When encount…
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…
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
Shengchao Hu, Ziqing Fan, Li Shen +3
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…
Q-value Regularized Transformer for Offline Reinforcement Learning
Shengchao Hu, Ziqing Fan, Chaoqin Huang +4
Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action distribution…