Curiosity-driven recommendation strategy for adaptive learning via deep reinforcement learning |
| |
Authors: | Ruijian Han Kani Chen Chunxi Tan |
| |
Institution: | Department of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kong |
| |
Abstract: | 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. |
| |
Keywords: | adaptive learning curiosity-driven exploration Markov decision problem recommendation system reinforcement learning |
|
|