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arXiv Machine Learning By Amir Mesbah, Di Wu

Before We Adapt: The Zero-Shot Generalization Bottleneck in Continual World Models

Read the original on arXiv Machine Learning →

The paper investigates how model-based reinforcement learning can support continual learning by reusing prior knowledge. It introduces a contextual Markov decision process that separates behavioral and physical context, and studies zero‑shot reuse in FourRooms tasks using Dyna‑Q and TD‑MPC. The experiments show that zero‑shot reuse degrades under physical context changes and that this degradation is strongly tied to the observation representation, highlighting representation generalization as a key bottleneck.

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