The action value, commonly referred to as the Q-value, represents the expected return (cumulative future reward) of taking a particular action in a given state and then following a specific policy afterward. Mathematically, it’s expressed as Q(s, a), where *s* is the current state and *a* is the action. Q-values help the agent make informed decisions by estimating the long-term benefit of each possible action.
In value-based reinforcement learning algorithms like Q-learning, the agent maintains and updates a table or function that estimates these Q-values. Over time, as the agent interacts with the environment and receives rewards, it adjusts its Q-value estimates to more accurately reflect reality. The policy the agent follows can be derived from these values by selecting the action with the highest estimated Q-value — a method known as greedy policy. However, to balance exploration and exploitation, agents often use strategies like ε-greedy or softmax based on Q-values. Action values are central to many RL algorithms because they bridge the gap between immediate rewards and long-term strategy.