Teaching Reinforcement Learning Agents with Adaptive Instructional Systems.

Traditionally, adaptive instructional systems (AISs) are built to instruct human students. However, they are not the only students that might benefit from an AIS. The field of reinforcement learning (RL), a subfield of machine learning, studies the instruction of synthetic students called agents, by means of various algorithms. In this paper, we advocate the use of an AIS as a conceptual framework to design and teach RL agents. We form our argument by deconstructing what it means to build and use an AIS for a human student, and discuss how the various concepts and relationships may apply to RL agents. We illustrate our findings by means of examples from the reinforcement learning literature and show a domain implementation of an AIS for RL agents.