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Density-Guided Bootstrap Boundaries for Latent World-Model Reinforcement Learning

arXiv cs.LG ↗ · 4d ago Cached

The paper introduces density-guided bootstrap boundaries, an adaptive mechanism for controlling imagined-return depth in latent world-model RL, proving contraction of the gated Bellman operator and showing improved sample efficiency within DreamerV3 across continuous-control and Atari tasks.

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