Reinforcement Learning (RL) is a key component in developing AI agents that can autonomously learn from interaction with an environment. In RL, agents are trained to maximize a cumulative reward by taking actions based on states they encounter. The agent's goal is to discover the most effective strategies through trial and error, allowing it to improve its behavior over time. RL is especially useful in scenarios where explicit supervision (like labeled data) is unavailable. Examples include robotics, game AI, and recommendation systems. Deep reinforcement learning, which combines RL with deep neural networks, has led to significant advancements, such as in AlphaGo, where an AI agent learned to play Go at a superhuman level.