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.

使用梯度范数奖励信号在深度强化学习的自动课程学习（ACL）中的影响研究。通过分析多个强化学习环境，发现梯度范数奖励对于教师模型制定具有挑战性且可达成的学习序列相当有效，进一步提高了学生的性能，加速了学习过程并改进了任务的泛化和适应性。这些研究结果突显出梯度范数信号在创建更高效和稳健的 ACL 系统中的潜力，为课程学习和强化学习领域的研究开辟了新的方向。