Using Deep Reinforcement Learning to Build Intelligent Tutoring Systems

This work proposes a novel method for building agents that can teach human users actions in various applications, considering both continuous and discrete input/output spaces and the multi-modal behaviors and learning curves of humans. While our method is presented and evaluated through a video game, it can be adapted to many other kinds of applications. Our method has two main actors: a teacher and a student. The teacher is first trained using reinforcement learning techniques to approach the ideal output in the target application, while still keeping the multi-modality aspects of human minds. The suggestions are provided online, at application runtime, using texts, images, arrows, etc. An intelligent tutoring system proposing actions to students considering a limited budget of attempts is built using Actor-Critic techniques. Thus, the method ensures that the suggested actions are provided only when needed and are not annoying for the student. Our evaluation is using a 3D video game, which captures all the proposed requirements. The results show that our method improves the teacher agents over the state-of-the-art methods, has a beneficial impact over human agents, and is suitable for real-time computations, without significant resources used.