Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions. Over time, it learns to choose actions that maximize its total reward, similar to how a child learns from their experiences. In this process, the agent explores different strategies and gradually improves its performance. This approach is used in various applications, such as training robots, playing video games, and optimizing traffic systems. By continuously learning from its successes and failures, the agent becomes more effective at achieving its goals.