دانلود کتاب Introduction to Unity ML-Agents: Understand the Interplay of Neural Networks and Simulation Space Using the Unity ML-Agents Package

Demystify the creation of efficient AI systems using the model-based reinforcement learning Unity ML-Agents - a powerful bridge between the world of Unity and Python.

We will start with an introduction to the field of AI, then discuss the progression of AI and where we are today. We will follow this up with a discussion of moral and ethical considerations. You will then learn how to use the powerful machine learning tool and investigate different potential real-world use cases. We will examine how AI agents perceive the simulated world and how to use inputs, outputs, and rewards to train efficient and effective neural networks. Next, you'll learn how to use Unity ML-Agents and how to incorporate them into your game or product. This book will thoroughly introduce you to ML-Agents in Unity and how to use them in your next project.

Explore the world of machine learning through Unity ML-Agents. In this book, you’ll learn about the impact of artificial intelligence and learn to build a reinforcement learning agent using the Unity ML-Agents package. It’s strongly recommended to go into this book with a solid understanding of the Unity engine and C#. The instructional chapters are written for Microsoft Windows 10, and some steps may vary across operating systems. The book also includes a sample repository with the code we will cover in this book and the solution to the challenge proposed in Chapter 8.

Machine Learning, neural networks, Deep Learning, and Artificial Intelligence are all words that you’ve already heard. While these terms are similar, they do have differences. Artificial Intelligence is a field of computer science in which we give a machine the ability to algorithmically process data and make decisions. On the other hand, Machine Learning is a subset of Artificial Intelligence covering the process in which a machine learns to think, much like the human brain in a process called reinforcement learning.