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Beyond Conservatism: Recoverability-Conditioned Exploration for Model-Based Imitation Learning
Model-based imitation learning (MBIL) improves real-environment interaction efficiency by optimizing policies on imagined rollouts from a learned world model. However, the gap between model-induced and real-environment occupancies makes policy learning sensitive to model error. Conservative MBIL mit
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