Book Image

Unity Artificial Intelligence Programming - Fourth Edition

By : Dr. Davide Aversa, Aung Sithu Kyaw, Clifford Peters
Book Image

Unity Artificial Intelligence Programming - Fourth Edition

By: Dr. Davide Aversa, Aung Sithu Kyaw, Clifford Peters

Overview of this book

Developing Artificial Intelligence (AI) for game characters in Unity 2018 has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from the 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 your game's worlds and characters. This fourth edition with Unity will help you break down AI into simple concepts to give you a fundamental understanding of the topic to build upon. 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. Further on, you'll learn how to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM). Next, you'll learn how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. You'll then learn how to implement simple ?ocks and crowd dynamics, which are key AI concepts in Unity. Moving on, you'll learn how to implement a behavior tree through a game-focused example. Lastly, you'll apply all the concepts in the book to build a popular game.
Table of Contents (13 chapters)

The Unity Machine Learning Agents Toolkit

The Unity Machine Learning Agents Toolkit (ML-Agents Toolkit for short) is a collection of software and plugins that help writing 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. In a very simplistic way, reinforcement learning is the algorithmic equivalent of training a dog. If you want to teach a dog some trick, you give him a command and then, when the dog does what you expect, you give him a reward. The reward tells your dog that it is responding correctly to a command and, therefore, the next time it hears the same command, it will do the same thing to get a new reward:

In reinforcement learning, you can also punish your agent...