Using Deep Reinforcement Learning to Build Intelligent Tutoring Systems

被引:0
|
作者
Paduraru, Ciprian [1 ]
Paduraru, Miruna [1 ]
Iordache, Stefan [1 ]
机构
[1] Univ Bucharest, Bucharest, Romania
关键词
Tutorial System; Reinforcement Learning; Actor-Critic; TD3; Games;
D O I
10.5220/0011267400003266
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
This work proposes a novel method for building agents that can teach human users actions in various applications, considering both continuous and discrete input/output spaces and the multi-modal behaviors and learning curves of humans. While our method is presented and evaluated through a video game, it can be adapted to many other kinds of applications. Our method has two main actors: a teacher and a student. The teacher is first trained using reinforcement learning techniques to approach the ideal output in the target application, while still keeping the multi-modality aspects of human minds. The suggestions are provided online, at application runtime, using texts, images, arrows, etc. An intelligent tutoring system proposing actions to students considering a limited budget of attempts is built using Actor-Critic techniques. Thus, the method ensures that the suggested actions are provided only when needed and are not annoying for the student. Our evaluation is using a 3D video game, which captures all the proposed requirements. The results show that our method improves the teacher agents over the state-of-the-art methods, has a beneficial impact over human agents, and is suitable for real-time computations, without significant resources used.
引用
收藏
页码:288 / 298
页数:11
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