Imitation Learning (IL) is a paradigm where an agent learns to perform tasks by observing and replicating demonstrations from an expert, rather than learning through trial-and-error with explicit reward signals. It is closely related to policy-distillation in RL settings and serves as a foundation for interactive-imitation-learning and broader knowledge tran...
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Imitation Learning
Imitation Learning (IL) is a paradigm where an agent learns to perform tasks by observing and replicating demonstrations from an expert, rather than learning through trial-and-error with explicit reward signals. It is closely related to policy-distillation in RL settings and serves as a foundation for interactive-imitation-learning and broader knowledge transfer methods. In the LLM context, supervised fine-tuning on expert outputs can be viewed as a form of imitation learning.
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- policy-distillation — Policy distillation can be seen as a form of imitation learning in RL
- interactive-imitation-learning — Extension of IL with interactive feedback
- knowledge-distillation — Knowledge distillation shares conceptual foundations with imitation learning