Imitation Learning
Imitation Learning is a machine learning paradigm where an agent learns to perform tasks by observing and mimicking expert demonstrations, rather than through trial-and-error reinforcement learning. It involves training a model to replicate the behavior of an expert, often using supervised learning techniques on demonstration data. This approach is particularly useful when designing reward functions for complex tasks is difficult or when expert knowledge is readily available.
Developers should learn Imitation Learning when building AI systems for robotics, autonomous vehicles, or game AI where expert demonstrations exist and reward engineering is challenging. It's valuable for tasks requiring safe, efficient learning from human experts, such as surgical robotics or industrial automation, and when quick policy initialization is needed before fine-tuning with reinforcement learning.