We covered the basics about Machine Learning and ML-Agents in this chapter by starting to introduce Machine Learning and the more common learning models, including Reinforcement Learning. After that, we looked at a game example with a cannon, where simple ML can be applied to solve the velocity required to strike a specific distance. Next, we quickly introduced ML-Agents and pulled the required code down from GitHub. This allowed us to run one of the more interesting examples in this book and explore the inner workings of the Heuristics brain. Then, we laid the foundations for a simple scene and set up the environment we will use over the next couple of chapters. Finally, we completed the chapter by setting up a simple Academy, Agent, and Brain, which were used to operate a multi-armed bandit using a Player brain.
In the next chapter, we will continue with our Bandit example and extend the problem to a contextual bandit, which is our first step toward Reinforcement Learning and building ML algorithms.
