Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. Rather than being told what to do, the agent receives rewards or penalties based on its actions and learns optimal behavior through trial and error.
RL sits at the intersection of AI, control theory, and behavioral psychology — making it ideal for scenarios that require dynamic problem-solving, adaptability, and continuous learning.