مقدمه‌ای بر ML-Agents یونیتی: درک تعامل شبکه‌های عصبی و شبیه‌سازی در فضای استفاده از بسته ML-Agents یونیتی

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. Table of Contents About the Author About the Technical Reviewers Acknowledgments Introduction Chapter 1: Introduction What Is Machine Learning? How We Use Machine Learning in the Modern Day Serving Content Recommendations Autonomous Vehicles Power and Electrical Grid Management Vaccines and Medical Drugs Farming Security and Surveillance Military City Planning Prerequisites Conclusion Chapter 2: History of AI and Where We Are Today The People Who Shaped Artificial Intelligence Alan Mathison Turing John McCarthy Marvin Lee Minsky Guido van Rossum Modern-Day Companies Paving the Future of AI Python Software Foundation Nvidia IBM Google Tesla OpenAI How AI Has Evolved in Games, from Chess to Dota 2 So, Where Are We Now with AI in Game Development? GitHub Copilot A Neural State Machine for Character-Scene Interactions BLOOM, a BigScience Initiative Conclusion Chapter 3: The Future of AI and Ethical Implications The Future of AI Law and Justice Healthcare Taxes and Governance Life Extension and Brain-Computer Interfaces Entertainment Avoiding a Bad Future Bias and Why We Need Diverse Datasets So, What Is Bias in AI? Why We Need Diverse Datasets Discussing the Moral and Ethical Implications Why AI? Flavors of AI AI Road Map and Classification Reactive Machines Limited Memory Theory of Mind Artificial General Intelligence (AGI) Self-Aware Artificial Superintelligence (ASI) Machine Learning with Unity ML-Agents Reinforcement Learning Imitation Learning Neuroevolution Practical Use Cases for Unity ML-Agents Learning How to Build Machine Learning Agents Self-Driving Cars Game AI Robotics Simulated Space for Agent Training Training Gym for Agents Conclusion Chapter 4: Dopamine for Machines Dopamine Dopamine in Humans Dopamine in Animals Dopamine in Machines Training Reinforcement Learning Agents How and When to Reward Your ML-Agents A Sound Reward System Makes for Great ML-Agents How Reward Systems Influence Training Time Various Aspects of Rewarding and Punishing ML-Agents Team-Based Rewards Conclusion Chapter 5: ML-Agents Setup Unity Setup New Project Setup ML-Agents Unity Package Setup Installing the ML-Agents Extensions Package Opening the Example GitHub Project Creating a GitHub Issue Python Setup Creating a Virtual Environment Installing ML-Agents and Dependencies Validating Our ML-Agents Installation with Samples Conclusion Chapter 6: Unity ML-Agents ML-Agent Components Behavior Parameters Behavior Name Vector Observations Actions Model Behavior Type Team Id Use Child Sensors Observable Attributes The Decision Requester Learning Environments The Agent Agent Override Methods override void Initialize() override void CollectObservations(VectorSensor sensor) override void OnActionReceived(ActionBuffers actionBuffers) override void OnEpisodeBegin() override void Heuristic(in ActionBuffers actionsOut) void RequestDecision() void AddReward(float increment) void SetReward(float reward) void EndEpisode() Inputs and Outputs Inputs, Observations, and Sensors So How Do We Create Observations? Collecting Observations Using the Observable Attribute Creating Sensors Building Sensors GetObservationSpec Vector Observation Spec Visual Observation Spec Variable Length Observation Spec Write ObservationWriter Writer[index] = observation AddList(IList , Int32) Add(Vector3, Int32) Add(Quaternion, Int32) Add(Vector4, Int32) GetCompressedObservation Update Reset GetCompressionSpec GetName Visual Observations Actions Continuous Discrete Heuristics Rewards Training an Agent Conclusion Chapter 7: Creating Your First AI in Unity Planning an Agent The Avoidance Sample Reward Scheme Observation Plans Actions Planning Expected Challenges Building Your First ML-Agent The Grid Sensor The Ray Perception Sensor Building the Environment Understanding Hyperparameters Training Your Agent Duplicating Your Training Zones TensorBoard and Why It’s Essential for Training Connecting Stand-Alone Builds to Python Exporting and Loading Your Model Conclusion Chapter 8: Solve a Challenge with AI The Challenge Grazer Agents Predator Agents Bonus Objective Before You Start Other Techniques to Consider CL (Curriculum Learning) BC (Behavioral Cloning) Self-Play Tips Conclusion Chapter 9: Next Steps Explore Additional ML-Agent Functionality Documentation Additional Reading Conclusion Index 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. What You Will Learn Understand machine learning, its history, capabilities, and expected progression Gives a step-by-step guide to creating your first AI Presents challenges of varying difficulty, along with tips to reinforce concepts covered Broad concepts within AI Who Is This Book For Tthose interested in machine learning using Unity ML-Agents. To get the best out of this book, you should have a fundamental understanding of C#, some background in Python, and are well versed in Unity.