Summary

In this chapter, we took our first step in the world of reinforcement learning. We covered some of the fundamental concepts and terminology of the field, including the agent, the policy, the value function, and the reward. We  also covered basic topics in deep learning and implemented a simple convolutional neural network using TensorFlow.

The field of reinforcement learning is vast and ever-expanding; it would be impossible to cover all of it in a single book. We do, however, hope to equip you with the practical skills and the necessary experience to navigate this field.

The following chapters will consist of individual projects—we will use a combination of reinforcement learning and deep learning algorithms to tackle several tasks and problems. We will build agents that will learn to play Go, explore the world of Minecraft, and play Atari video games. We hope you are ready to embark on this exciting learning journey!