Inverse Reinforcement Learning (also summarized as IRL) is an area of machine learning where the goal is to determine the underlying reward function a subject (often an agent) uses based on their observed behavior in an environment. This is essentially the opposite of the more commonly understood Reinforcement Learning (RL), where the agent learns to optimize its behavior to maximize a known reward function.

In IRL, the challenge is that the reward function is unknown and needs to be inferred. This is useful in scenarios where it's difficult to define what the reward should be but easier to observe and measure behavior that is considered optimal. For example, in autonomous driving, instead of programming every potential good driving habit, engineers can use IRL to learn what good driving looks like by observing human drivers.

The process involves observing an expert performing a task, gathering data on their actions and the states they pass through, and then using algorithms to find a reward function that would make those actions appear optimal. The inferred reward function can then guide the development of an AI system that mimics the expert’s behavior.

IRL is complex because multiple reward functions can often explain the same observed behavior, leading to challenges in ensuring the learned rewards are truly representative of the expert’s intentions. Despite these complexities, IRL holds significant promise for improving how machines learn to replicate human behavior in domains ranging from robotics to economics.