Definition
Reinforcement learning (RL) is a type of machine learning where an agent learns to make a sequence of decisions by trying to maximize a reward it receives for its actions. The agent explores its environment, taking actions and observing the outcomes, adjusting its strategy based on whether those outcomes lead to positive or negative rewards. This approach is valuable for automating complex decision-making processes and optimizing performance in dynamic systems. For instance, RL can be used to train autonomous vehicles to navigate traffic by rewarding them for safe and efficient driving. Unlike supervised learning, which relies on labeled data, RL learns through trial and error without explicit guidance on correct actions. This concept is relevant for advanced AI and machine learning certifications.