Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation

强化学习 计算机科学 目标导向 马尔可夫决策过程 图形 路径(计算) 人工智能 机器学习 任务(项目管理) 目标设定 马尔可夫过程 理论计算机科学 统计 经济 管理 程序设计语言 社会心理学 数学 心理学
作者
Qingyao Li,Wei Xia,Liang Yin,Jian Shen,Renting Rui,Weinan Zhang,Xianyu Chen,Ruiming Tang,Yong Yu
标识
DOI:10.1145/3583780.3614897
摘要

Goal-oriented Learning path recommendation aims to recommend learning items (concepts or exercises) step-by-step to a learner to promote the mastery level of her specific learning goals. By formulating this task as a Markov decision process, reinforcement learning (RL) methods have demonstrated great power. Although extensive research efforts have been made, previous methods still fail to recommend effective goal-oriented paths due to the under-utilizing of goals. Specifically, it is mainly reflected in two aspects: (1)The lack of goal planning. When learners have multiple goals with different difficulties, the previous methods can't fully utilize the difficulties and dependencies between goal learning items to plan the sequence of achieving these goals, making the path chaotic and inefficient; (2)The lack of efficiency in goal achieving. When pursuing a single goal, the path may contain learning items unrelated to the goal, which makes realizing a certain goal inefficient. To address these challenges, we present a novel Graph Enhanced Hierarchical Reinforcement Learning (GEHRL) framework for goal-oriented learning path recommendation. The framework divides learning path recommendation into two parts: sub-goal selection(planning) and sub-goal achieving(learning item recommendation). Specifically, we employ a high-level agent as a sub-goal selector to select sub-goals for the low-level agent to achieve. The low-level agent in the framework is to recommend learning items to the learner. To make the path only contain goal-related learning items to improve the efficiency of achieving the goal, we develop a graph-based candidate selector to constrain the action space of the low-level agent based on the sub-goal and knowledge graph. We also develop test-based internal reward for low-level training so that the sparsity problem of external reward can be alleviated. Extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.
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