Unverified50% confidenceFactExact time
The DAgger (Dataset Aggregation) method has experts provide corrective labels for biased states during model execution, in order to address the distribution shift problem of behavior cloning.
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10/12/2026
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UnverifiedThe DAgger algorithm alleviates the distribution shift problem in imitation learning through iterative requests for expert corrections.78% similarUnverifiedThe model in this study fixes features and inequality directions, and is used to analyze the learning of decision stump thresholds73% similarUnverifiedResearchers design trade-off scenarios in which models choose between achieving a higher score and avoiding being described as experiencing suffering, in order to observe the model's decision-making tendencies.70% similarUnverifiedBehavioral cloning suffers from the covariate shift problem, and Ross et al. proposed the DAgger algorithm (Dataset Aggregation) to address this issue, allowing the learner to continuously query the expert during execution and expand the training set.70% similarUnverifiedThis study models the data selection problem as a high-dimensional regression scenario with multiple heterogeneous data sources, and employs a weighted ridge regression estimator to fuse these sources.70% similar
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