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

Using the ML-Agents Toolkit – a basic example

Now that everything is installed, we can start using the ML-Agents Toolkit. First, let's explain the basic architecture of an ML-Agents scene.

An ML-Agents scene is called a learning environment. The learning environment is a standard Unity scene and contains two main elements:

  • The agent: Obviously, the Agent is the central object in the ML-Agents Toolkit. An agent is an object that performs an action, receives information from the environment, and can receive rewards for actions. To create an Agent, you need to subclass the Agent class and write the behavior for the agent. For instance, if the Agent is a car, we need to write how the car is controlled by the input and how we can reward and penalize the car (for example, we can reward the vehicle for going above a certain speed and punish it when it goes off-road). A learning environment can have as many agents as you like.
  • The academy: This component is a singleton...