Values and policy

Before we start talking about policy gradients, let's refresh our minds with the common characteristics of the methods covered in part two of this book. The central topic in value iteration and Q-learning is the value of the state (V) or value of the state and action (Q). Value is defined as the discounted total reward that we can gather from this state or by issuing this particular action from the state. If we know the value, our decision on every step becomes simple and obvious: we just act greedily in terms of value, and that guarantees us a good total reward at the end of the episode. So, the values of states (in the case of the value iteration method) or state + action (in the case of Q-learning) stand between us and the best reward. To obtain these values, we have used the Bellman equation, which expresses the value on the current step via the values on the next step.

In Chapter 1, What Is Reinforcement Learning?, we defined the entity that tells us...