Inverse Reinforcement Learning (IRL) is a process used to infer the underlying reward function of an agent by observing its behavior, rather than explicitly defining the rewards. This approach is particularly useful in scenarios where specifying a reward function is difficult, allowing for the automation of learning complex tasks by mimicking expert behavior.