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Imitation Learning is a technique where models learn to perform tasks by mimicking expert behaviors. This approach is often used when defining explicit reward functions is challenging. It accelerates learning by using pre-collected datasets of expert demonstrations, reducing the need for trial-and-error in initial learning phases.

Reinforcement Learning involves agents learning to make decisions by interacting with an environment to maximize cumulative rewards. It's foundational in fields where sequential decision-making is crucial, like gaming, autonomous vehicles, and robotics. RL uses methods like Q-learning and policy gradient to iteratively improve agent performance based on feedback from the environment.