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Better Slots, Better Worlds: Representation Quality&Robustness in Object-Centric World Models
Under unseen distribution shifts, the OCWM with well-bound slots is more robust overall than the end-to-end trained scene-centric LeWM, while DINO-WM, built on similar frozen pretrained features, remains competitive -- pointing to pretrained features as a key contributor to robustness.
Forecasting in Offline Reinforcement Learning for Non-stationary Environments
F orecasting in Non-stationary O ffline RL (F ORL), a framework that combines zero-shot forecasting with the agent’s experience, aims to bridge the gap between offline RL and the complexities of real-world, non-stationary environments.