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Forget reward function dependencies – this new approach to contextual bandits with latent state dynamics achieves stronger regret bounds by directly modeling hidden state dependencies and adaptively estimating HMM parameters.
Pre-training with Dual Latent World Models unlocks significant performance gains in autonomous driving tasks by learning holistic Gaussian-centric representations.
