Real-life value iteration
The improvements that we got in the FrozenLake environment by switching from the cross-entropy method to the value iteration method are quite encouraging, so it's tempting to apply the value iteration method to more challenging problems. However, let's first look at the assumptions and limitations that our value iteration method has.
We will start with a quick recap of the method. On every step, the value iteration method does a loop on all states, and for every state, it performs an update of its value with a Bellman approximation. The variation of the same method for Q-values (values for actions) is almost the same, but we approximate and store values for every state and action. So, what's wrong with this process?
The first obvious problem is the count of environment states and our ability to iterate over them. In value iteration, we assume that we know all states in our environment in advance, can iterate over them, and can store value...
