Offsiteteam

Action Value (Q-value)

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.

Mentioned in blog posts:

Ready to Bring
Your Idea to Life?
Fill out the form below to tell us about your project.
We'll contact you promptly to discuss your needs.
We received your message!
Thank you!