Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances

Understand

Imitation learning (IL) enables agents to acquire skills by observing and replicating the behavior of one or multiple experts.

• In recent years, advances in deep learning have significantly expanded the capabilities and scalability of imitation learning across a range of domains, where expert data can range from full state-action trajectories to partial observations or unlabeled sequences.
• Alongside this growth, novel approaches have emerged, with new methodologies being developed to address longstanding challenges such as generalization, covariate shift, and demonstration quality.
• In this survey, we review the latest advances in imitation learning research, highlighting recent trends, methodological innovations, and practical applications.