Saliency-Guided Representation with Consistency Policy Learning for Visual Unsupervised Reinforcement Learning

arXiv:2604.05931v1 Announce Type: cross Abstract: Zero-shot unsupervised reinforcement learning (URL) offers a promising direction for building generalist agents capable of generalizing to unseen tasks without additional supervision. Among existing approaches, successor representations (SR) have emerged as a prominent paradigm due to their effectiveness in structured, low-dimensional settings. However, SR methods struggle to scale to high-dimensional visual environments. Through empirical analys