As an agent takes actions and moves through an environment, it learns to map the observed state of the environment to an action. An agent will choose an action in a given state based on a \"Q-value\", which is a weighted reward based on the expected highest long-term reward. A Q-Learning Agent learns to perform its task such that the recommended action maximizes the potential future rewards. This method is considered an \"Off-Policy\" method, meaning its Q values are updated assuming that the best action was chosen, even if the best action was not chosen.