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arXiv Machine Learning By Xinfei Wang, Shanchen Pang, Chenhao Zhang, Shudong Wang, Wenhao Ji, Haiyuan Gui, Meng Han, Xiaojian Liao

DaCe-DT: Data-Centric Offline Multi-Task Reinforcement Learning via Adaptive Prompts and Trajectory Correction for Heterogeneous Tasks

Read the original on arXiv Machine Learning →

DaCe-DT is an offline multi‑task reinforcement learning framework that tackles data‑level bottlenecks by introducing length‑gated prompt masking, retrieval‑augmented prompt construction, and value‑adaptive return calibration. These mechanisms adapt prompts to diverse task complexities, ensure semantic relevance of prompt segments, and correct misleading supervision from fragmented trajectories. Experiments on Meta‑World show that DaCe‑DT outperforms state‑of‑the‑art methods, improving performance by 11.73% on optimal datasets and 13.34% on suboptimal datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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