Value-based methods in reinforcement learning focus on estimating the value of state-action pairs to determine the best actions to take. The primary goal of these methods is to find the optimal value function, which helps the agent evaluate the expected long-term return from any given state or state-action pair.
One of the most well-known value-based methods is Q-learning, where the agent learns a Q-value (action-value function) for each state-action pair. The Q-value represents the expected future rewards for taking a particular action in a given state. The agent updates its Q-values based on the reward it receives, gradually refining its policy to choose actions that lead to higher rewards.
Value iteration and policy iteration are other examples of value-based methods. These methods are efficient for problems with discrete state-action spaces but may struggle with high-dimensional or continuous environments.