Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment. The agent takes actions and receives feedback in the form of rewards or penalties. The goal is to develop a strategy, or policy, that maximizes cumulative rewards over time. RL involves exploration (trying new actions) and exploitation (using known successful actions) to improve decision-making. It's widely used in applications like autonomous driving, game playing, and robotics. Discover how RL algorithms train agents to adapt and optimize their behavior to achieve the best outcomes in complex scenarios