Reinforcement Learning (RL) is a machine learning technique focused on optimizing decision-making through interaction with an environment, where an agent takes actions to maximize long-term rewards. RL involves a cycle of observation, action, reward, and state transition, requiring a balance between exploration and exploitation. The Markov Decision Process (MDP) provides a mathematical framework for RL, while Q-learning helps develop optimal policies using action-value updates. RL has applications in gaming, robotics, autonomous vehicles, healthcare, and finance, but challenges such as high data requirements, computational costs, safety concerns, and delayed rewards persist.