most citedExploring Training on Heterogeneous Data with Mixture of Low-rank Adapters

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cs.LG2024

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…

cs.LG2024

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…