Development of Reinforcement Learning Robot Teaching Material That Integrates Virtual and Real Environments to Experience Reality Gap

Abstract

In this paper, we developed and evaluated the effectiveness of a line-tracing robot teaching material using reinforcement learning for students in the Department of Mechanical Engineering. Currently, most AI and reinforcement learning materials are based on simulations.However, it is important to have teaching materials that allow students to learn not only how to simulate, but also how to adapt it to the actual environment. In this study, taking a line-trace robot as an example, we developed teaching materials that also allow students to learn about the reality gap that occurs between the simulated and real environments. In order to evaluate the teaching materials, we verified through lessons using the developed line-tracing robot whether the teaching materials were comprehension and whether they were effective in promoting understanding of Q-learning. An analysis of the questionnaire conducted after the lecture confirmed developed material was effective in promoting understanding of the reality gap.