Why policy-based methods?
The objective of reinforcement learning is to find the optimal policy, which is the policy that provides the maximum return. So far, we have learned several different algorithms for computing the optimal policy, and all these algorithms have been value-based methods. Wait, what are value-based methods? Let's recap what value-based methods are, and the problems associated with them, and then we will learn about policy-based methods. Recapping is always good, isn't it?
With value-based methods, we extract the optimal policy from the optimal Q function (Q values), meaning we compute the Q values of all state-action pairs to find the policy. We extract the policy by selecting an action in each state that has the maximum Q value. For instance, let's say we have two states s0 and s1 and our action space has two actions; let the actions be 0 and 1. First, we compute the Q value of all the state-action pairs, as shown in the following table...
