Inverse reinforcement learning

Inverse Reinforcement Learning (IRL) is a machine learning technique aimed at inferring the reward function that an agent is optimizing based on its observed behavior. Unlike traditional reinforcement learning, where the reward function is known and the goal is to learn the optimal policy, IRL focuses on understanding the underlying motivations or objectives driving the observed actions. This method is particularly useful in fields such as robotics, autonomous driving, and human-computer interaction, where it helps in designing systems that can replicate expert behavior.