For us to determine the best policy, we first need a method to evaluate the given policy for a state. We can use a method of evaluating the policy by searching through all of the states of an MDP and further evaluating all actions. This will provide us with a value function for the given state that we can then use to perform successive updates of a new value function iteratively. Mathematically, we can then use the previous Bellman optimality equation and derive a new update to a state value function, as shown here:
In the preceding equation, the 
symbol represents an expectation and denotes the expected state value update to a new value function. Inside this expectation, we can see this dependent on the returned reward plus the previous discounted value for the next state given an already chosen action. That means that our algorithm will iterate over...
