Arvofunktio

Arvofunktio, often translated as "value function" in English, is a concept central to reinforcement learning. It represents the expected future reward that an agent can receive starting from a particular state, or from a state-action pair. The value function guides the agent in making decisions by quantifying the desirability of different states or actions.

There are generally two types of value functions. The state-value function, denoted as V(s), estimates the expected return from a given state 's' assuming the agent follows a specific policy. The action-value function, denoted as Q(s, a), estimates the expected return from taking a specific action 'a' in state 's' and then following a specific policy thereafter.

The goal in many reinforcement learning algorithms is to learn an optimal value function, which represents the maximum possible expected future reward. Once the optimal value function is known, an optimal policy can be derived, dictating the best action to take in any given state to maximize long-term rewards. Value functions are typically learned iteratively through interaction with the environment, using techniques like dynamic programming, Monte Carlo methods, or temporal-difference learning.