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Decoupled Q-learning achieves state-of-the-art performance on continuous control by adaptively growing action grids, but surprisingly, the adaptive growth itself is not the key driver of success.
TSFMs for predictive control fail to capture system dynamics without explicit control excitation in their inference context, even with low forecasting error.