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.

中文翻译：

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通过深度强化学习进行自适应学习的好奇心驱动推荐策略。

自适应学习系统中推荐策略的设计侧重于利用当前可用的信息为学习者提供针对个人的学习指导。作为人类行为的关键动机，好奇心本质上是探索知识和寻求信息的动力。从心理启发的角度来看，我们在强化学习框架内提出了一种好奇心驱动的推荐策略，从而实现了高效且令人愉悦的个性化学习路径。具体来说，来自精心设计的预测模型的好奇心奖励被生成，以模拟人们对知识空间的熟悉程度。鉴于这种好奇心奖励，我们应用演员-评论家方法直接通过神经网络近似策略。