Q-function

In Reinforcement Learning, Q-function is a mathematical function that determines the expected utility of taking a particular action in a given state and following a particular policy thereafter. It maps a state-action pair to a real number representing the expected cumulative reward obtained by taking that action in that state and following the policy thereafter. The Q-function is used to determine the optimal policy for an agent to take in a given environment. The optimal policy is the one that maximizes the expected cumulative reward over time. Q-learning is a popular algorithm used to learn the Q-function in a model-free manner, i.e., without requiring a model of the environment. The Q-function is often represented as a table or a neural network.
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