Foundations of Deep Reinforcement Learning: Theory and Practice in Python

1. Introduction to Reinforcement Learning

In this chapter we introduce the main concepts in reinforcement learning. We start by looking at some simple examples to build intuitions about the core components of a reinforcement learning problem—namely, an agent and an environment.

In particular, we will look at how an agent interacts with an environment to optimize an objective. We will then define these more formally and define reinforcement learning as a Markov Decision Process. This is the theoretical foundation of reinforcement learning.

Next, we introduce the three primary functions an agent can learn—a policy, value functions, and a model. We then see how learning these functions gives rise to different families of deep reinforcement learning ...