Inverse reinforcement learning is a machine learning paradigm focused on inferring the reward function that rational agents optimize, given observations of their behavior. This field addresses the inverse problem to standard reinforcement learning by seeking to determine the underlying goals or preferences that explain observed actions, rather than finding an optimal policy for a predefined reward. Its significance lies in enabling agents to learn complex tasks from expert demonstrations by recovering the implicit objective, facilitating behavioral analysis, and overcoming the challenge of manual reward specification in complex environments.