All around us, our perception of learning and intellect is being challenged daily with the advent of new and emerging technologies. From self-driving cars, playing Go and Chess, to computers being able to beat humans at classic Atari games, the advent of a group of technologies we colloquially call Machine Learning have come to dominate a new era in technological growth – a new era of growth that has been compared with the same importance as the discovery of electricity and has already been categorized as the next human technological age.
This book is intended to introduce you to a very small slice of that new era in a fun and informative way using the Machine Learning Agents platform called ML-Agents from Unity. We will first explore some basics of Machine Learning and ML-Agents. Then, we will cover training and specifically Reinforcement Learning and Q Learning. After that, we will learn how to use Keras to build a Neural Network that we will evolve into a Deep Q-Network. From there, we will look at various ways to improve the Deep Q-Network with different training strategies. This will lead us to our first example, where we train an agent to play a more complex game. Then, finally, we will finish with a look at a multi-agent example that allows agents to compete with or against each other.
In our first chapter, we will take a gradual introduction to ML and ML-Agents. Here is what we will cover in this chapter:
- Machine Learning
- ML-Agents
- Running an example
- Creating an environment
- Academy, Agent, and Brain
Let's get started, and in the next section, we will introduce what Machine Learning is and the particular aspect of ML we plan to focus on in this book.
