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今天的主题是：Student-Informed Teacher Training

Summary

This research introduces a novel framework for imitation learning that addresses the challenge of teacher-student asymmetry. The method jointly trains a teacher and student policy, where the teacher learns behaviors easily imitated by the student despite the student's limited observability. This is achieved by adding a penalty term to the teacher's reward function and incorporating a supervised alignment step. The effectiveness of the proposed framework is demonstrated across diverse robotic tasks, including maze navigation, quadrotor flight, and robotic manipulation, consistently outperforming baseline imitation learning methods. The results highlight the importance of considering student capabilities during teacher training to improve overall learning efficiency and performance.

这项研究提出了一种新框架，用于解决模仿学习中教师与学生之间的不对称性问题。该方法联合训练教师策略和学生策略，其中教师学习出一种行为，使学生在观察能力受限的情况下也能轻松模仿。为此，在教师的奖励函数中加入了惩罚项，并引入了监督对齐步骤。该框架在多种机器人任务中展现了其有效性，包括迷宫导航、四旋翼飞行和机器人操作，并在性能上始终优于基线模仿学习方法。研究结果突出了在教师训练过程中考虑学生能力的重要性，以提升整体学习效率和性能。

原文链接：https://arxiv.org/abs/2412.09149