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arXiv Machine Learning By Zhenya Liu, Yuxin Chen

Active Curriculum Refinement for Reinforcement Learning

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The paper introduces PATH, a curriculum‑learning framework that actively refines reinforcement learning by exploiting the directed acyclic curriculum graph formed by prerequisite relations among environments. PATH first expands coverage by sampling diverse curriculum paths and then reallocates training toward regions that remain unmastered. Experiments across diverse environments demonstrate that explicitly leveraging the graph structure leads to strong robustness and generalization.

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