Reinforcement learning is a type of machine learning where an agent learns through trial and error, receiving rewards or penalties based on its actions. It’s particularly effective for optimizing decision-making in complex, ever-changing environments. RL can be applied in scenarios such as dynamic pricing, resource allocation, and personalized recommendations, offering adaptive strategies that respond to real-world changes.
