Imitation learning is a machine learning paradigm where an agent learns to perform a task by observing demonstrations provided by an expert. This approach investigates techniques for inferring a policy or control strategy directly from observed state-action pairs or state trajectories, typically without relying on an explicit reward signal. Its significance lies in enabling agents to acquire complex behaviors in environments where defining a suitable reward function is difficult or impractical.