Regret minimization is a research area in decision theory and machine learning that focuses on developing algorithms and strategies to minimize the overall regret or disappointment experienced by an individual or system when making a sequence of decisions. This is typically achieved by learning from past decisions and experiences in order to make better choices in the future. Regret minimization algorithms are often used in online learning, game theory, and reinforcement learning settings to optimize decision-making processes and improve overall performance.