Curiosity-driven recommendation strategy for adaptive learning via deep reinforcement learning

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.