Q-Learning

The algorithm updates a table or function representing action values based on rewards observed through interaction with an environment. A policy can then select actions with high expected long-term reward, making Q-learning useful for discrete control and decision problems. The technique fits into the broader machine-learning lifecycle that includes data preparation, training, evaluation, deployment, and monitoring.