Reinforcement learning (RL) is learning from interaction with an environment, from the consequences of action, rather than from explicit teaching. RL become popular in the 1990s within machine learning and artificial intelligence, but also within operations research and with offshoots in psychology and neuroscience.

Most RL research is conducted within the mathematical framework of Markov decision processes (MDPs). MDPs involve a decision-making agent interacting with its environment so as to maximize the cumulative reward it receives over time. The agent perceives aspects of the environment's state and selects actions. The agent may estimate a value function and use it to construct better and better decision-making policies over time.

RL algorithms are methods for solving this kind of problem, that is, problems involving sequences of decisions in which each decision affects what opportunities are available later, in which the effects need not be deterministic, and in which there are long-term goals. RL methods are intended to address the kind of learning and decision making problems that people and animals face in their normal, everyday lives.