亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI5应助Ma采纳,获得10
12秒前
忧伤的绍辉完成签到 ,获得积分10
13秒前
隐形曼青应助易四夕采纳,获得10
17秒前
56秒前
易四夕发布了新的文献求助10
1分钟前
1分钟前
1分钟前
Ma发布了新的文献求助10
1分钟前
1分钟前
随机子发布了新的文献求助10
1分钟前
1分钟前
Ava应助科研通管家采纳,获得10
1分钟前
科目三应助科研通管家采纳,获得10
1分钟前
科研通AI2S应助科研通管家采纳,获得10
1分钟前
Ma发布了新的文献求助10
2分钟前
Ma完成签到,获得积分10
2分钟前
2分钟前
易四夕发布了新的文献求助10
3分钟前
3分钟前
3分钟前
英姑应助王大壮采纳,获得10
3分钟前
SciGPT应助科研通管家采纳,获得10
3分钟前
3分钟前
4分钟前
4分钟前
Mine发布了新的文献求助10
4分钟前
王大壮发布了新的文献求助10
4分钟前
Mine完成签到,获得积分10
4分钟前
郗妫完成签到,获得积分10
4分钟前
王大壮发布了新的文献求助10
4分钟前
科研通AI5应助Mine采纳,获得30
4分钟前
4分钟前
852应助美好颜采纳,获得10
4分钟前
纯情女大完成签到 ,获得积分10
5分钟前
5分钟前
5分钟前
FashionBoy应助科研通管家采纳,获得30
5分钟前
丘比特应助科研通管家采纳,获得10
5分钟前
斯文败类应助科研通管家采纳,获得10
5分钟前
赘婿应助科研通管家采纳,获得10
5分钟前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968492
求助须知:如何正确求助?哪些是违规求助? 3513278
关于积分的说明 11167214
捐赠科研通 3248660
什么是DOI,文献DOI怎么找? 1794386
邀请新用户注册赠送积分活动 875030
科研通“疑难数据库(出版商)”最低求助积分说明 804638