DAgger

Dataset Aggregation — an interactive imitation learning algorithm that addresses distributional shift in behavioral cloning. DAgger iteratively: (1) runs the current policy, (2) queries the expert for corrections at the visited states, and (3) aggregates the new labeled data with the existing dataset for re-training. This on-policy data collection ensures the policy learns to recover from its own mistakes.