Value Function

In reinforcement learning, a value function is a crucial component that estimates the expected return or future rewards an agent can obtain from a given state or state-action pair. It serves as a guide for the agent to make decisions, helping it understand which actions are likely to yield the most benefit over time. There are two main types of value functions: the state value function, which evaluates the value of being in a particular state, and the action value function, which assesses the value of taking a specific action in a given state. Value functions are commonly used in algorithms like Q-learning and Deep Q-Networks (DQN) to optimize the agent's performance in various environments.