Learn Unity Ml Agents Fundamentals Of Unity Machine Learning
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| Author | : Micheal Lanham |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 197 |
| Release | : 2018-06-30 |
| Genre | : Computers |
| ISBN | : 1789131863 |
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.
| Author | : Micheal Lanham |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 420 |
| Release | : 2020-01-03 |
| Genre | : Computers |
| ISBN | : 1839216778 |
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.
| Author | : Micheal Lanham |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 379 |
| Release | : 2019-03-30 |
| Genre | : Computers |
| ISBN | : 1788998766 |
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.
| Author | : Laura Graesser |
| Publisher | : Addison-Wesley Professional |
| Total Pages | : 629 |
| Release | : 2019-11-20 |
| Genre | : Computers |
| ISBN | : 0135172489 |
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.
| Author | : Georgios N. Yannakakis |
| Publisher | : Springer |
| Total Pages | : 350 |
| Release | : 2018-02-17 |
| Genre | : Computers |
| ISBN | : 3319635190 |
This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading.
| Author | : Micheal Lanham |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 394 |
| Release | : 2017-06-30 |
| Genre | : Computers |
| ISBN | : 1787120805 |
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.
| Author | : Micheal Lanham |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 394 |
| Release | : 2020-10-20 |
| Genre | : Computers |
| ISBN | : 1492075760 |
Working with AI is complicated and expensive for many developers. That's why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video. Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quickly get up to speed with Google Cloud Platform, whether you want to build an AI assistant or a simple business AI application. Learn key concepts for data science, machine learning, and deep learning Explore tools like Video AI and AutoML Tables Build a simple language processor using deep learning systems Perform image recognition using CNNs, transfer learning, and GANs Use Google's Dialogflow to create chatbots and conversational AI Analyze video with automatic video indexing, face detection, and TensorFlow Hub Build a complete working AI agent application
| Author | : Jonathan Linowes |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 383 |
| Release | : 2021-08-16 |
| Genre | : Computers |
| ISBN | : 1838982965 |
Explore the world of augmented reality development with the latest features of Unity and step-by-step tutorial-style examples with easy-to-understand explanations Key FeaturesBuild functional and interactive augmented reality applications using the Unity 3D game engineLearn to use Unity's XR and AR components, including AR Foundation and other standard Unity featuresImplement common AR application user experiences needed to build engaging applicationsBook Description Augmented reality applications allow people to interact meaningfully with the real world through digitally enhanced content. The book starts by helping you set up for AR development, installing the Unity 3D game engine, required packages, and other tools to develop for Android (ARCore) and/or iOS (ARKit) mobile devices. Then we jump right into the building and running AR scenes, learning about AR Foundation components, other Unity features, C# coding, troubleshooting, and testing. We create a framework for building AR applications that manages user interaction modes, user interface panels, and AR onboarding graphics that you will save as a template for reuse in other projects in this book. Using this framework, you will build multiple projects, starting with a virtual photo gallery that lets you place your favorite framed photos on your real-world walls, and interactively edit these virtual objects. Other projects include an educational image tracking app for exploring the solar system, and a fun selfie app to put masks and accessories on your face. The book provides practical advice and best practices that will have you up and running quickly. By the end of this AR book, you will be able to build your own AR applications, engaging your users in new and innovative ways. What you will learnDiscover Unity engine features for building AR applications and gamesGet up to speed with Unity AR Foundation components and the Unity APIBuild a variety of AR projects using best practices and important AR user experiencesUnderstand the core concepts of augmented reality technology and development for real-world projectsSet up your system for AR development and learn to improve your development workflowCreate an AR user framework with interaction modes and UI, saved as a template for new projectsWho this book is for This augmented reality book is for game developers interested in adding AR capabilities to their games and apps. The book assumes beginner-level knowledge of Unity development and C# programming, familiarity with 3D graphics, and experience in using existing AR applications. Beginner-level experience in developing mobile applications will be helpful to get the most out of this AR Unity book.
| Author | : Andreas Sudmann |
| Publisher | : transcript Verlag |
| Total Pages | : 335 |
| Release | : 2019-10-31 |
| Genre | : Social Science |
| ISBN | : 3839447194 |
After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms?
| Author | : Yoon Hyup Hwang |
| Publisher | : |
| Total Pages | : 350 |
| Release | : 2018-06-14 |
| Genre | : Computers |
| ISBN | : 9781788996402 |
Power your C# and .NET applications with exciting machine learning models and modular projects Key Features Produce classification, regression, association, and clustering models Expand your understanding of machine learning and C# Get to grips with C# packages such as Accord.net, LiveCharts, and Deedle Book Description Machine learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising; from finance to scientifc research. This book will help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects. You will get an overview of the machine learning systems and how you, as a C# and .NET developer, can apply your existing knowledge to the wide gamut of intelligent applications, all through a project-based approach. You will start by setting up your C# environment for machine learning with the required packages, Accord.NET, LiveCharts, and Deedle. We will then take you right from building classifcation models for spam email fltering and applying NLP techniques to Twitter sentiment analysis, to time-series and regression analysis for forecasting foreign exchange rates and house prices, as well as drawing insights on customer segments in e-commerce. You will then build a recommendation model for music genre recommendation and an image recognition model for handwritten digits. Lastly, you will learn how to detect anomalies in network and credit card transaction data for cyber attack and credit card fraud detections. By the end of this book, you will be putting your skills in practice and implementing your machine learning knowledge in real projects. What you will learn Set up the C# environment for machine learning with required packages Build classification models for spam email filtering Get to grips with feature engineering using NLP techniques for Twitter sentiment analysis Forecast foreign exchange rates using continuous and time-series data Make a recommendation model for music genre recommendation Familiarize yourself with munging image data and Neural Network models for handwritten-digit recognition Use Principal Component Analysis (PCA) for cyber attack detection One-Class Support Vector Machine for credit card fraud detection Who this book is for If you're a C# or .NET developer with good knowledge of C#, then this book is perfect for you to get Machine Learning into your projects and make smarter applications.
