Active Curriculum Refinement for Reinforcement Learning
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Computer Science > Machine Learning
Title:Active Curriculum Refinement for Reinforcement Learning
Abstract:In many reinforcement learning (RL) domains, environments are connected by prerequisite relations, such as difficulty-increasing edits or parameter increments, which induce a directed acyclic curriculum graph (DAG). Although this structure is often exploited only implicitly, explicitly modeling it can improve training. We introduce PATH, a curriculum-learning framework that performs active learning over the curriculum graph. PATH first expands coverage by sampling diverse curriculum paths and then reallocates training toward regions that remain unmastered. Experiments across diverse environments show that PATH explicitly leverages the graph structure to achieve strong robustness and generalization.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.26469 [cs.LG] |
| (or arXiv:2608.26469v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.26469
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the International Conference on Machine Learning 2026 |
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