In this video, we build on our basic understanding of reinforcement learning by exploring the workflow. We cover what an environment is and some of the benefits of training within a simulated environment. We cover what we ultimately want our agent to do and how crafting a reward function incentivizes the agent to do just that. And lastly, we introduce the need to choose a way to represent a policy—how we want to structure the parameters and logic that make up the decision-making part of the agent.