The introduction explains the importance of increasing memorization in order to deepen knowledge. Therefore, there was done a lot of work in order to gain better understanding of how human memorization works. Some memory models tried to formalize so-called spacing effect, meaning that spaced review improves the memorization.

Nowadays, learning tools like flashcards are very popular. But systems similar to SuperMemo lack reasoning or mathematical understanding of the above-mentioned problem. Review scheduling can be quite challenging in the similar applications. This is related to the trade-off between showing a new content or the old ones.

As DRL achieved quite good results in training agent, the authors of this paper assume that similar can be achieved if they formulate spaced repetition as a task. The agent will focus on students performance and will try to understand the factors that influence student learning and use them in order to improve student learning. The authors aim to use review scheduling algorithm with model-free reinforcement learning where neural network tries to approximate following three teaching policies:

1. Directly operates on raw observations of outcomes and intervals between reviews
2. Scales to large numbers of items
3. Easily adapts to different learning objectives and student models.

The key problem will be DRL sample complexity. As the authors claim: "learning a policy requires a large number of interactions with the environment. Training an autonomous tutor from scratch using a large number of interactions with real students is infeasible."