Book Image

Unity Artificial Intelligence Programming - Fifth Edition

By : Dr. Davide Aversa
Book Image

Unity Artificial Intelligence Programming - Fifth Edition

By: Dr. Davide Aversa

Overview of this book

Developing artificial intelligence (AI) for game characters in Unity has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from basic techniques to cutting-edge machine learning-powered agents. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating game worlds and characters. The updated fifth edition of Unity Artificial Intelligence Programming starts by breaking down AI into simple concepts. Using a variety of examples, the book then takes those concepts and walks you through actual implementations designed to highlight key concepts and features related to game AI in Unity. As you progress, you’ll learn how to implement a finite state machine (FSM) to determine how your AI behaves, apply probability and randomness to make games less predictable, and implement a basic sensory system. Later, you’ll understand how to set up a game map with a navigation mesh, incorporate movement through techniques such as A* pathfinding, and provide characters with decision-making abilities using behavior trees. By the end of this Unity book, you’ll have the skills you need to bring together all the concepts and practical lessons you’ve learned to build an impressive vehicle battle game.
Table of Contents (17 chapters)
1
Part 1:Basic AI
6
Part 2:Movement and Navigation
11
Part 3:Advanced AI

The Unity Machine Learning Agents Toolkit

The Unity Machine Learning Agents Toolkit (ML-Agents Toolkit) is a collection of software and plugins that help developers write autonomous game agents powered by machine learning algorithms. You can explore and download the source code at the GitHub repository at https://github.com/Unity-Technologies/ml-agents.

The ML-Agents Toolkit is based on the reinforcement learning algorithm. Simplistically, reinforcement learning is the algorithmic equivalent of training a dog. For example, if you want to teach a dog some trick, you give him a command, and then, when the dog does what you expect, you reward him. The reward tells your dog that it responded correctly to the command, and therefore, the next time it hears the same command, it will do the same thing to get a new reward.

Note

In reinforcement learning, you can also punish your agent when doing the wrong things, but in the dog-training example, I can assure you that punishment is...