You can teach agents efficient exploration by having a privileged teacher label actions conditioned on the student's real exploration history—no RL needed, just supervised learning with asymmetric information access.
ASTEROID is a student-teacher learning framework that teaches agents to explore efficiently under partial observability without reinforcement learning. A privileged teacher with full state information labels actions based on the student's actual exploration history, allowing the student to learn when and how to gather information.