This paper investigates the impact of using gradient norm reward signals in the context of Automatic Curriculum Learning (ACL) for deep reinforcement learning (DRL). We introduce a framework where the teacher model, utilizing the gradient norm information of a student model, dynamically adapts the learning curriculum. This approach is based on the hypothesis that gradient norms can provide a nuanced and effective measure of learning progress. Our experimental setup involves several reinforcement learning environments (PointMaze, AntMaze, and AdroitHandRelocate), to assess the efficacy of our method. We analyze how gradient norm rewards influence the teacher's ability to craft challenging yet achievable learning sequences, ultimately enhancing the student's performance. Our results show that this approach not only accelerates the learning process but also leads to improved generalization and adaptability in complex tasks. The findings underscore the potential of gradient norm signals in creating more efficient and robust ACL systems, opening new avenues for research in curriculum learning and reinforcement learning.

翻译：本文研究了在深度强化学习（DRL）的自动课程学习（ACL）背景下，使用梯度范数奖励信号的影响。我们提出了一种框架，其中教师模型利用学生模型的梯度范数信息，动态调整学习课程。该方法基于一个假设，即梯度范数能够提供一种细微且有效的学习进度衡量标准。我们的实验设置包括多个强化学习环境（PointMaze、AntMaze和AdroitHandRelocate），以评估所提方法的有效性。我们分析了梯度范数奖励如何影响教师构建具有挑战性但可实现的学习序列的能力，并最终提升学生模型的性能。结果表明，该方法不仅能加速学习过程，还能提高复杂任务中的泛化能力和适应性。这些发现凸显了梯度范数信号在构建更高效、更稳健的ACL系统中的潜力，为课程学习和强化学习的研究开辟了新途径。