Also known as: Q learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of actions in states without requiring a model of the environment's dynamics. It seeks to approximate an optimal Q-function, often using the Bellman equation, so an agent can choose actions that maximize expected return.
In reinforcement learning, an algorithm that allows an agent to learn the optimal Q-function of a Markov decision process by applying the Bellman equation. The Markov decision process models an environment.