Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or punishments. The agent learns to maximize its cumulative reward by exploring different actions and their consequences. This area of research is inspired by behavioral psychology and has applications in various fields such as robotics, gaming, finance, and healthcare. Some popular algorithms used in reinforcement learning include Q-learning, Deep Q Networks (DQN), and Policy Gradient methods.