Exploratory Data Analysis

Exploration versus exploitation is a core dilemma in artificial intelligence, particularly in reinforcement learning. Exploration involves trying new actions to gather more information about the environment, potentially discovering more effective strategies. Exploitation, on the other hand, leverages known information to maximize immediate rewards. Balancing these ensures optimal learning and decision-making over time, avoiding local optima and improving overall performance.