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Function approximation in machine learning, particularly in reinforcement learning, involves approximating a function rather than explicitly storing values for all possible inputs. In function approximation, Q(s, a) represents the action-value function, which gives the expected cumulative reward of taking action 'a' in state 's' and following a certain policy thereafter. Traditionally, in methods like Q-learning, a table is used to store Q-values for all possible state-action pairs. However, in environments with large or continuous state and action spaces, this approach becomes impractical. 

Function approximation methods aim to generalize the learned values across similar states and actions. One common approach is to use a function approximator, such as a neural network, to represent the Q-function. The network takes state-action pairs as input and outputs the estimated Q-value. This allows the model to generalize its knowledge to unseen states and actions. By combining various features of the state and action into a function, the model can learn a more compact and general representation of the Q-values, making it more scalable to complex environments.

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