Väärtusfunktsioonide

Väärtusfunktsioonid, often translated as value functions, are a fundamental concept in reinforcement learning and decision theory. They represent the expected cumulative future reward an agent can achieve from a given state or state-action pair. In essence, a value function quantifies the desirability of being in a particular situation from the perspective of an agent aiming to maximize its rewards.

There are two primary types of value functions: state-value functions and action-value functions. A state-value function, denoted as V(s), estimates the expected return starting from state 's' and following a particular policy thereafter. This policy dictates the agent's behavior, i.e., how it chooses actions in different states. An action-value function, denoted as Q(s, a), estimates the expected return starting from state 's', taking action 'a', and then following a particular policy. The Q-function is often more useful in practical reinforcement learning algorithms because it directly tells the agent the value of taking a specific action in a specific state, enabling it to choose the optimal action.

The core idea behind value functions is their recursive definition, often expressed through Bellman equations. These equations state that the value of a state or state-action pair is equal to the immediate reward received plus the discounted value of the next state or state-action pair, averaged over all possible next states and actions according to the policy. By iteratively updating these value functions, agents can learn optimal policies that maximize their long-term cumulative rewards, even in complex and uncertain environments. Value functions are crucial for many reinforcement learning algorithms, including Q-learning and SARSA, which are used in diverse applications such as robotics, game playing, and recommendation systems.