Value Functions

Value functions are a core concept in reinforcement learning and decision theory that estimate the expected cumulative reward an agent can achieve from a given state or state-action pair. They quantify the long-term desirability of states or actions, guiding agents toward optimal behavior by predicting future outcomes. In reinforcement learning, value functions are typically learned through algorithms like dynamic programming, Monte Carlo methods, or temporal-difference learning.

Also known as: State-Value Function, Action-Value Function, V-function, Q-function, Expected Return

Why learn Value Functions?

Developers should learn value functions when working on reinforcement learning projects, such as training AI agents for games, robotics, or autonomous systems, as they provide a mathematical foundation for evaluating and improving policies. They are essential for solving Markov decision processes (MDPs) and are used in algorithms like Q-learning and policy gradient methods to optimize decision-making in uncertain environments.

Learning Resources

• book: Reinforcement Learning: An Introduction (Sutton & Barto)
• video: DeepMind's Introduction to Reinforcement Learning
• docs: OpenAI Spinning Up in Deep RL - Value Functions
• course: Coursera: Reinforcement Learning Specialization
• tutorial: Towards Data Science: Understanding Value Functions