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 action that maximizes the value function in each state. For example, in Q-learning, the agent learns the Q-value for each state-action pair and then selects the action with the highest Q-value as its policy. Policy gradient methods, on the other hand, directly learn the policy without explicitly learning a value function. These methods parameterize the policy and then update the parameters to improve the policy's performance, typically by using gradient ascent to maximize the expected reward. For example, the REINFORCE algorithm directly estimates the gradient of the expected reward with respect to the policy parameters and then updates the parameters in the direction of the gradient. In essence, value-based methods learn 'how good' a state or action is and then use this information to choose actions, while policy gradient methods directly learn 'what to do' in each state. Policy gradient methods are often more effective in high-dimensional or continuous action spaces, where it can be difficult to learn an accurate value function. However, they can also be more sensitive to hyperparameter tuning and may exhibit higher variance compared to value-based methods.