Finding optimal policies with Dynamic Programming

Dynamic Programming (DP) is a base for many RL algorithms. The main paradigm of DP algorithms is to use the state- and action-value functions as tools to find the optimal policy, given a fully-known model of the environment. In this section, we'll see how to do that.

Policy evaluation

We'll start with policy evaluation, or how to compute the state-value function, , given a specific policy, π. This task is also known as prediction. As a reminder, we'll assume that the state-value function is a table. We'll implement policy evaluation using the state-value Bellman equation we defined in the Bellman equations section. Let's start:

1. Input the following...