Effective curiosity-driven exploration in 3D environments requires both a persistent, continuously-updated world model and episodic memory of the agent's trajectory—without these, agents waste effort revisiting forgotten states instead of discovering new regions.
This paper shows how to make AI agents explore 3D environments effectively using curiosity-driven learning. The key insight is that agents need two things: a persistent 3D map of the world that updates continuously, and memory of where they've been.