# What is function approximation in reinforcement learning?

Source: https://zilliz.com/ai-faq/what-is-function-approximation-in-reinforcement-learning
Author: zilliz

Function approximation in reinforcement learning is the technique of approximating the value function or policy when the state or action space is too large to represent explicitly in a table. Instead of maintaining a table of values for all states or state-action pairs, function approximation uses a parameterized model, such as a neural network, to estimate the value function or policy.

For example, in Deep Q-learning, the Q-function is approximated by a deep neural network that maps states and actions to their corresponding Q-values. This allows the agent to scale to more complex environments where tabular methods would be inefficient or impractical.

Function approximation is essential in high-dimensional state spaces (e.g., pixel data in games or images), allowing RL to work on tasks that are beyond the reach of traditional tabular methods.