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
相关论文
共 50 条
  • [41] Intelligent Dynamic Spectrum Access Using Deep Reinforcement Learning for VANETs
    Wang, Yonghua
    Li, Xueyang
    Wan, Pin
    Shao, Ruiyu
    [J]. IEEE SENSORS JOURNAL, 2021, 21 (14) : 15554 - 15563
  • [42] Intelligent handover decision scheme using double deep reinforcement learning
    Mollel, Michael S.
    Abubakar, Attai Ibrahim
    Ozturk, Metin
    Kaijage, Shubi
    Kisangiri, Michael
    Zoha, Ahmed
    Imran, Muhammad Ali
    Abbasi, Qammer H.
    [J]. PHYSICAL COMMUNICATION, 2020, 42
  • [43] Intelligent Residential Energy Management System Using Deep Reinforcement Learning
    Mathew, Alwyn
    Roy, Abhijit
    Mathew, Jimson
    [J]. IEEE SYSTEMS JOURNAL, 2020, 14 (04): : 5362 - 5372
  • [44] Using Intelligent Tutoring Systems in Instruction and Education
    Gharehchopogh, Farhad Soleimanian
    Khalifelu, Zeynab Abbasi
    [J]. EDUCATION AND MANAGEMENT TECHNOLOGY, ICEMT 2011, 2011, 13 : 250 - 254
  • [45] Intelligent Control for Switched Systems with Time Delay via Deep Reinforcement Learning
    Song, Ruijia
    Wang, Bolan
    Cheng, Haoyu
    Huang, Hanqiao
    Yan, Jie
    [J]. PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 6160 - 6165
  • [46] Deep Reinforcement Learning Algorithms in Intelligent Infrastructure
    Serrano, Will
    [J]. INFRASTRUCTURES, 2019, 4 (03)
  • [47] Intelligent Optimization Control for Air Starting Systems Based on Deep Reinforcement Learning
    Peng, Jin
    Li, Xin
    Guo, Zhongyu
    Yang, Wenda
    Xu, Hongzhang
    Qi, Yiwen
    [J]. 2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 2589 - 2594
  • [48] Intelligent Tutoring Systems
    Nkambou, Roger
    [J]. EDUCATIONAL TECHNOLOGY & SOCIETY, 2010, 13 (01): : 1 - 2
  • [49] INTELLIGENT TUTORING SYSTEMS
    ANDERSON, JR
    BOYLE, CF
    REISER, BJ
    [J]. SCIENCE, 1985, 228 (4698) : 456 - 462
  • [50] A Recommendation Module based on Reinforcement Learning to an Intelligent Tutoring System for Software Maintenance
    Francisco, Rodrigo Elias
    Silva, Flavin de Oliveira
    [J]. CSEDU: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED EDUCATION - VOL 1, 2022, : 322 - 329