一种用于建模异构学生教学策略的广义学徒学习框架
A Generalized Apprenticeship Learning Framework for Modeling Heterogeneous Student Pedagogical Strategies
AI总结：
针对智能辅导系统中异构教学策略建模难题，提出通用学徒学习框架EM-EDM，通过处理连续状态空间与异构奖励函数，在极少演示下有效诱导教学策略，性能优于AL与DRL基线。
摘要：
在智能辅导系统（ITSs）等电子学习环境中，一个关键挑战是高效地诱导出有效的教学策略。虽然深度强化学习（DRL）通常存在样本效率低和奖励函数设计困难的问题，但学徒学习（AL）算法可以克服这些缺点。然而，大多数AL算法无法处理异构性，因为它们假设所有演示都是由单一奖励函数驱动的同质策略生成的。此外，一些考虑异构性的AL算法通常无法泛化到大型连续状态空间，且仅适用于离散状态。在本文中，我们提出了EM-EDM，这是一个通用的AL框架，用于从给定的最优或近优演示中诱导出有效的教学策略，这些演示被假设是由异构奖励函数驱动的。我们在两个不同但相关、涉及教学动作预测的任务上，将我们提出的EM-EDM诱导的策略与四个基于AL的基线和两个由DRL诱导的策略进行了有效性比较。总体结果表明，对于这两个任务，EM-EDM在所有性能指标上均优于四个AL基线和两个DRL基线。这表明EM-EDM能够通过管理大型连续状态空间并适应处理多样且异构的奖励函数（仅需极少给定演示），从而有效地建模复杂的学生教学决策过程。
英文摘要：
A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) often suffers from sample inefficiency and reward function design difficulty, Apprenticeship Learning(AL) algorithms can overcome them. However, most AL algorithms can not handle heterogeneity as they assume all demonstrations are generated with a homogeneous policy driven by a single reward function. Still, some AL algorithms which consider heterogeneity, often can not generalize to large continuous state space and only work with discrete states. In this paper, we propose an expectation-maximization(EM)-EDM, a general AL framework to induce effective pedagogical policies from given optimal or near-optimal demonstrations, which are assumed to be driven by heterogeneous reward functions. We compare the effectiveness of the policies induced by our proposed EM-EDM against four AL-based baselines and two policies induced by DRL on two different but related tasks that involve pedagogical action prediction. Our overall results showed that, for both tasks, EM-EDM outperforms the four AL baselines across all performance metrics and the two DRL baselines. This suggests that EM-EDM can effectively model complex student pedagogical decision-making processes through the ability to manage a large, continuous state space and adapt to handle diverse and heterogeneous reward functions with very few given demonstrations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
