主动优化学习路径，让强化学习更高效地攻克难题。
Active Curriculum Refinement for Reinforcement Learning
- 基于课程图结构，主动选择最优学习路径。
- 在多种环境中提升训练鲁棒性与泛化能力。
- 适合需要渐进式训练的复杂强化学习任务。
在许多强化学习领域中，环境之间存在先决关系，例如难度递增的修改或参数逐步提升，形成有向无环课程图（DAG）。尽管这种结构常被隐式利用，但显式建模可显著改善训练效果。本文提出PATH框架，通过在课程图上进行主动学习来优化训练过程：首先采样多样化的课程路径以扩大覆盖范围，然后将训练资源重新分配至尚未掌握的区域。在多个不同环境中的实验表明，PATH能有效利用课程图结构，实现强鲁棒性和良好泛化性能。
原文摘要 · Abstract (English)
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.
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