Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Unity Artificial Intelligence Programming

You're reading from   Unity Artificial Intelligence Programming Add powerful, believable, and fun AI entities in your game with the power of Unity

Arrow left icon
Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781803238531
Length 308 pages
Edition 5th Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Dr. Davide Aversa Dr. Davide Aversa
Author Profile Icon Dr. Davide Aversa
Dr. Davide Aversa
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1:Basic AI
2. Chapter 1: Introduction to AI FREE CHAPTER 3. Chapter 2: Finite State Machines 4. Chapter 3: Randomness and Probability 5. Chapter 4: Implementing Sensors 6. Part 2:Movement and Navigation
7. Chapter 5: Flocking 8. Chapter 6: Path Following and Steering Behaviors 9. Chapter 7: A* Pathfinding 10. Chapter 8: Navigation Mesh 11. Part 3:Advanced AI
12. Chapter 9: Behavior Trees 13. Chapter 10: Procedural Content Generation 14. Chapter 11: Machine Learning in Unity 15. Chapter 12: Putting It All Together 16. Other Books You May Enjoy

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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime