Reinforcement learning (RL) is a machine learning technique that enables an agent to learn a sequence of decisions through trial-and-error interactions with an environment, receiving feedback in the form of rewards or penalties. Unlike supervised and unsupervised learning, RL does not rely on labeled data but rather focuses on maximizing cumulative rewards through a balance of exploration and exploitation. RL algorithms are divided into model-based and model-free categories, with Q-learning and policy gradient being popular model-free methods. Q-learning uses a table to update action-value functions, while policy gradient directly learns the policy using gradient ascent. The technique is particularly suitable for decision-making under dynamic and uncertain conditions, making it valuable in industries such as robotics, autonomous vehicles, and gaming. In robotics, RL optimizes complex behaviors and movements, while in autonomous vehicles, it helps in making informed decisions for safer operation. In gaming, RL has been used to enhance gameplay, as demonstrated by AlphaGo's success against top human players. Overall, RL offers unique advantages for automating complex processes and improving efficiency across various applications.