Dynamic programming is a very general method to efficiently solve problems that can be decomposed into overlapping sub-problems. If you have used any type of recursive function in your code, you might have already got some preliminary flavor of dynamic programming. Dynamic programming, in simple terms, tries to cache or store the results of sub-problems so that they can be used later if required, instead of computing the results again.
Okay, so how is that relevant here, you may ask. Well, they are pretty useful for solving a fully defined MDP, which means that an agent can find the most optimal way to act in an environment to achieve the highest reward using dynamic programming if it has full knowledge of the MDP! In the following table, you will find a concise summary of what the inputs and outputs are when we are interested in sequential prediction...
