  Human-centered AI considers human experiences with AI performance. While
abundant research has been helping AI achieve superhuman performance either by
fully automatic or weak supervision learning, fewer endeavors are experimenting
with how AI can tailor to humans' preferred skill level given fine-grained
input. In this work, we guide the curriculum reinforcement learning results
towards a preferred performance level that is neither too hard nor too easy via
learning from the human decision process. To achieve this, we developed a
portable, interactive platform that enables the user to interact with agents
online via manipulating the task difficulty, observing performance, and
providing curriculum feedback. Our system is highly parallelizable, making it
possible for a human to train large-scale reinforcement learning applications
that require millions of samples without a server. The result demonstrates the
effectiveness of an interactive curriculum for reinforcement learning involving
human-in-the-loop. It shows reinforcement learning performance can successfully
adjust in sync with the human desired difficulty level. We believe this
research will open new doors for achieving flow and personalized adaptive
difficulties.
