Sign inGet started
Paper · arXiv

Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration

Exploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven reinforcement learning addresses this via intrinsic rewards derived from the mismatch between the agent's predictive model of the world and reality. However, translating this intrinsic motivation to complex, photorealistic environments remains difficult, as agents can become trapped in local loops and receive fresh rewards for revisiting forgotten states. In this work, we demonstrate that this failure stems from a lack of spatial persistence and episodic context. We show that effective curiosity requires a model of the world that is persis...

AuthorsLily Goli, Justin Kerr, Daniele Reda et al.
Primary categorycs.LG
Published21 May 2026
Last updated at source21 May 2026
cs.LG
Embed this entry
<iframe src="https://silytics.si/embed/item/758826f9-4ec5-41a1-bf51-9fcdff79a683" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.