Deep Reinforcement Learning Hands-On

Markov decision processes

In this part of the chapter, we'll get familiar with the theoretical foundation of RL, which makes it possible to start moving toward the methods used to solve the RL problem. This section is important to understand the rest of the book and will ensure that you familiarize yourself with RL. First, we introduce you to the mathematical representation and notation of formalisms (reward, agent, actions, observations, and environment) we just discussed. Second, using this basis, we introduce you to the second-order notions of the RL language including state, episode, history, value, and gain, which will be used repeatedly to describe different methods later in the book. Finally, our description of Markov decision processes ...