Value-Based Methods

Value-based methods are a class of reinforcement learning algorithms that focus on estimating the value of states or state-action pairs to determine optimal policies, without explicitly modeling the environment's dynamics. They work by learning value functions, such as the state-value function or action-value function, which represent the expected cumulative reward from a given state or state-action pair. These methods are widely used in problems where the goal is to maximize long-term rewards through sequential decision-making.

Also known as: Value-Based Reinforcement Learning, Value Function Methods, Value Iteration Methods, Q-Learning, SARSA

Why learn Value-Based Methods?

Developers should learn value-based methods when building applications in artificial intelligence, robotics, or game development that require agents to learn optimal behaviors through trial and error, such as training AI for video games, autonomous systems, or recommendation engines. They are particularly useful in environments with discrete action spaces and when computational efficiency is a priority, as they often avoid the complexity of policy gradients or model-based approaches.

Learning Resources

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