Learn Unity ML-Agents – Fundamentals of Unity Machine Learning
Learn Unity ML-Agents – Fundamentals of Unity Machine Learning

Author: Micheal Lanham

Publisher: Packt Publishing Ltd

Published: 2018-06-30

Total Pages: 197

ISBN-13: 1789131863

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Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity Key Features Learn how to apply core machine learning concepts to your games with Unity Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games Learn How to build multiple asynchronous agents and run them in a training scenario Book Description Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem. What you will learn Develop Reinforcement and Deep Reinforcement Learning for games. Understand complex and advanced concepts of reinforcement learning and neural networks Explore various training strategies for cooperative and competitive agent development Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning. Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration Implement a simple NN with Keras and use it as an external brain in Unity Understand how to add LTSM blocks to an existing DQN Build multiple asynchronous agents and run them in a training scenario Who this book is for This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity. The reader will be required to have a working knowledge of C# and a basic understanding of Python.

Introduction to Unity ML-Agents
Introduction to Unity ML-Agents

Author: Dylan Engelbrecht

Publisher:

Published: 2023

Total Pages: 0

ISBN-13: 9781484294659

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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. You will: Understand machine learning, its history, capabilities, and expected progression Gain a step-by-step guide to creating your first AI Work with challenges of varying difficulty, along with tips to reinforce concepts covered Master broad concepts within AI.

Unity 2022 by Example
Unity 2022 by Example

Author: Scott H. Cameron

Publisher: Packt Publishing Ltd

Published: 2024-06-07

Total Pages: 596

ISBN-13: 1803237953

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Start building commercial and playable games such as 2D collection and adventure games, 3D FPS game in Unity with C#, and add AR/VR/MR experiences to them with this illustrated guide Key Features Create game apps, including a 2D adventure game, a 3D first-person shooter, and more Get up to speed with Unity Gaming Services available for creating commercially viable games Follow steps for publishing, marketing, and maintaining your games effectively Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnity 2022 by Example is a complete introduction to building games in Unity following a project-based approach. You’ll be introduced to the Unity game engine and the tools available for building and customizing a game exactly the way you want it, while maintaining a good code foundation to build upon. Once you get to grips with the fundamentals of Unity game development, you'll start creating a 2D collection game and an adventure game, followed by a 3D first person shooter game. Next, you’ll explore advanced topics, such as using machine learning to create AI-based enemy behavior, virtual reality for extending the first-person game, and augmented reality for developing a farming simulation game in a real-world setting. The book will help you gain hands-on knowledge of these topics as you build projects using the latest game tool kits. You'll also learn how to commercialize your game by publishing it to a distribution platform and maintain and support it throughout its lifespan. As you progress, you’ll gain real-world knowledge and experience by taking your games from conceptual design to completion. By the end of this Unity book, you’ll have strong foundational knowledge of how to structure a Unity project that is both maintainable and extensible for commercially released games.What you will learn Build game environments and design levels, and implement game mechanics using Unity's features Explore 3D game creation, focusing on gameplay mechanics and player animation Develop customizable game systems using object-oriented architecture Build an MR experience using the XR Interaction Toolkit while learning how to merge virtual and real-world elements Get up to speed with advanced AI interactions using sensors and Unity's machine learning toolkit, ML-Agents Implement dynamic content in games using Unity LiveOps services like Remote Config Who this book is for If you find yourself struggling with completing game projects in Unity and want to follow best practices while maintaining a good coding structure, then this book is for you. This book is also for aspiring game developers and hobbyists with some experience in developing games, who want to design basic playable and commercial games in Unity with a core loop, player verbs, simple mechanics, and win/lose conditions. Experience with the Unity Editor interface and implementing functionality by creating C# scripts is required to get the most out of this book.

Hands-On Deep Learning for Games
Hands-On Deep Learning for Games

Author: Micheal Lanham

Publisher: Packt Publishing Ltd

Published: 2019-03-30

Total Pages: 379

ISBN-13: 1788998766

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Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key FeaturesApply the power of deep learning to complex reasoning tasks by building a Game AIExploit the most recent developments in machine learning and AI for building smart gamesImplement deep learning models and neural networks with PythonBook Description The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning. What you will learnLearn the foundations of neural networks and deep learning.Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots. Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.Working with Unity ML-Agents toolkit and how to install, setup and run the kit.Understand core concepts of DRL and the differences between discrete and continuous action environments.Use several advanced forms of learning in various scenarios from developing agents to testing games.Who this book is for This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.

Unity Artificial Intelligence Programming
Unity Artificial Intelligence Programming

Author: Dr. Davide Aversa

Publisher: Packt Publishing Ltd

Published: 2018-11-30

Total Pages: 238

ISBN-13: 1789531454

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Unity 2018 provides game and app developers with a variety of tools to implement Artificial Intelligence(AI). Leveraging these tools via Unity's API allows limitless possibilities for creating your game's worlds and characters. This edition will break down AI into simple concepts to give you a fundamental understanding of the topic to build upon.

Unity Artificial Intelligence Programming
Unity Artificial Intelligence Programming

Author: Dr. Davide Aversa

Publisher: Packt Publishing Ltd

Published: 2022-03-28

Total Pages: 309

ISBN-13: 1803245212

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Learn and implement game AI in Unity to build smart environments and enemies with A* pathfinding, finite state machines, behavior trees, and the NavMesh Key Features Explore the latest Unity features to make AI implementation in your game easier Build richer and more dynamic games using AI concepts such as behavior trees and navigation meshes Implement character behaviors and simulations using the Unity Machine Learning toolkit Book Description 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. What you will learn Understand the basics of AI in game design Create smarter game worlds and characters with C# programming Apply automated character movement using pathfinding algorithm behaviors Implement character decision-making algorithms using behavior trees Build believable and highly efficient artificial flocks and crowds Create sensory systems for your AI world Become well-versed with the basics of procedural content generation Explore the application of machine learning in Unity Who this book is for This Unity artificial intelligence book is for Unity developers with a basic understanding of C# and the Unity Editor who want to expand their knowledge of AI Unity game development.

Hands-On Reinforcement Learning for Games
Hands-On Reinforcement Learning for Games

Author: Micheal Lanham

Publisher: Packt Publishing Ltd

Published: 2020-01-03

Total Pages: 420

ISBN-13: 1839216778

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Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is for If you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.

Foundations of Deep Reinforcement Learning
Foundations of Deep Reinforcement Learning

Author: Laura Graesser

Publisher: Addison-Wesley Professional

Published: 2019-11-20

Total Pages: 629

ISBN-13: 0135172489

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The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Game Audio Development with Unity 5.X
Game Audio Development with Unity 5.X

Author: Micheal Lanham

Publisher: Packt Publishing Ltd

Published: 2017-06-30

Total Pages: 394

ISBN-13: 1787120805

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Create 'AAA' quality game audio with new features and tools built for Unity About This Book Explore the basics of audio development in Unity to create spatial sound, mixing, effects, composition, adaptive audio and more. Leverage the Audio Mixer of Unity 5.x to create blockbuster sound and music for your game. Learn about developing professional audio for games with FMOD Studio and composing original music with Reaper. Build amazing audio synchronized graphic visualizations with Unity. Understand how real-time character lip syncing can be implemented. Who This Book Is For The ideal target audience for this book will be game developers, both Indie as well as semi pro. No prior knowledge of Unity and audio development is assumed, What You Will Learn Develop game audio and other audio effects with Unity Getting familiar with the new Audio Mixer introduced in Unity 5 Implement dynamic and adaptive audio using various tools and strategies Explore interesting ways to incorporate audio into a game with sound visualization Use 3rd party professional audio development tools like FMOD Compose original music and record vocals Understand and troubleshoot audio performance issues In Detail Game Audio is one of the key components in making a game successful and it is quite popular in the gaming industry. So if you are a game developer with an eye on capturing the gamer market then this book is the right solution for you. In this book, we will take you through a step by step journey which will teach you to implement original and engaging soundtracks and SFX with Unity 5.x. You will be firstly introduced to the basics of game audio and sound development in Unity. After going through the core topics of audio development: audio sources, spatial sound, mixing, effects, and more; you will then have the option of delving deeper into more advanced topics like dynamic and adaptive audio. You will also learn to develop dynamic and adaptive audio using the Unity Audio Mixer. Further, you will learn how professional third party tools like FMOD are used for audio development in Unity. You will then go through the creation of sound visualization techniques and creating your own original music using the simple yet powerful audio workstation Reaper. Lastly, you will go through tips, techniques and strategies to help you optimize game audio performance or troubleshoot issues. At the end of the book, you'll have gained the skills to implement professional sound and music. Along with a good base knowledge audio and music principles you can apply across a range of other game development tools. Style and approach This book will have a step by step practical approach where downloadable free games will be given with the book and readers will be free to work with them.

Deep Learning Illustrated
Deep Learning Illustrated

Author: Jon Krohn

Publisher: Addison-Wesley Professional

Published: 2019-08-05

Total Pages: 725

ISBN-13: 0135121728

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"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.