What is the central difference between policy gradient methods and value-based methods in reinforcement learning?

The central difference between policy gradient methods and value-based methods in reinforcement learning lies in how they approach the problem of finding an optimal policy. Value-based methods, such as Q-learning and SARSA, focus on learning an estimate of the optimal value function, which represents the expected cumulative reward for being in a given state or taking a specific action in a given state. These methods use the value function to indirectly derive a policy, typically by selecting the ac....