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Curiosity‐driven recommendation strategy for adaptive learning via deep reinforcement learning
Author(s) -
Han Ruijian,
Chen Kani,
Tan Chunxi
Publication year - 2020
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/bmsp.12199
Subject(s) - curiosity , reinforcement learning , computer science , knowledge space , space (punctuation) , artificial intelligence , artificial neural network , state space , path (computing) , machine learning , psychology , knowledge management , social psychology , mathematics , operating system , statistics , programming language
The design of recommendation strategies in the adaptive learning systems focuses on utilizing currently available information to provide learners with individual‐specific learning instructions. As a critical motivate for human behaviours, curiosity is essentially the drive to explore knowledge and seek information. In a psychologically inspired view, we propose a curiosity‐driven recommendation policy within the reinforcement learning framework, allowing for an efficient and enjoyable personalized learning path. Specifically, a curiosity reward from a well‐designed predictive model is generated to model one's familiarity with the knowledge space. Given such curiosity rewards, we apply the actor–critic method to approximate the policy directly through neural networks. Numerical analyses with a large continuous knowledge state space and concrete learning scenarios are provided to further demonstrate the efficiency of the proposed method.