Value-Based Methods are approaches in decision-making and learning that focus on maximizing the expected value of outcomes. These methods evaluate the potential benefits of different actions by estimating their associated rewards and costs. By prioritizing actions that yield the highest value, individuals or systems can make more informed choices. In the context of reinforcement learning, value-based methods involve learning a value function that predicts the expected reward for each action in a given state. This allows agents to select actions that lead to the most favorable long-term results, enhancing their ability to navigate complex environments effectively.