Reinforcement Learning Part 4: Expected Return, Value Functions, and Bellman Equations

In the previous post, we defined a policy, provided the foundational concept of a Markov Decision Process (MDP), and talked about trajectories. We’re going to combine these concepts with the idea of future discounted returns to create value functions. We now introduce two new concepts: state-value function (given by V(s)) that attempts to estimate the […]