Reinforcement learning is a type of machine learning where an agent interacts with its environment and learns to take actions to maximize rewards or minimize penalties. The agent learns through trial and error, receiving feedback in the form of rewards or punishments. Algorithms like Q-learning or Deep Q-Network (DQN) are commonly used in reinforcement learning.