Introduction To Unity Ml Agents
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Introduction To Unity Ml Agents
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Author : Dylan Engelbrecht
language : en
Publisher:
Release Date : 2023
Introduction To Unity Ml Agents written by Dylan Engelbrecht and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
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.
Learn Unity Ml Agents Fundamentals Of Unity Machine Learning
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Author : Micheal Lanham
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-30
Learn Unity Ml Agents Fundamentals Of Unity Machine Learning written by Micheal Lanham and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-30 with Computers categories.
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.
Deep Reinforcement Learning In Unity
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Author : Abhilash Majumder
language : en
Publisher: Apress
Release Date : 2020-12-02
Deep Reinforcement Learning In Unity written by Abhilash Majumder and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-02 with Computers categories.
Gain an in-depth overview of reinforcement learning for autonomous agents in game development with Unity. This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement learning. Then, you will move on to path finding and navigation meshes in Unity, setting up the ML Agents Toolkit (including how to install and set up ML agents from the GitHub repository), and installing fundamental machine learning libraries and frameworks (such as Tensorflow). You will learn about: deep learning and work through an introduction to Tensorflow for writing neural networks (including perceptron, convolution, and LSTM networks), Q learning with Unity ML agents, and porting trained neural network models in Unity through the Python-C# API. You will also explore the OpenAI Gym Environment used throughout the book. Deep Reinforcement Learning in Unity provides a walk-through of the core fundamentals of deep reinforcement learning algorithms, especially variants of the value estimation, advantage, and policy gradient algorithms (including the differences between on and off policy algorithms in reinforcement learning). These core algorithms include actor critic, proximal policy, and deep deterministic policy gradients and its variants. And you will be able to write custom neural networks using the Tensorflow and Keras frameworks. Deep learning in games makes the agents learn how they can perform better and collect their rewards in adverse environments without user interference. The book provides a thorough overview of integrating ML Agents with Unity for deep reinforcement learning. What You Will Learn Understand how deep reinforcement learning works in games Grasp the fundamentals of deep reinforcement learning Integrate these fundamentals with the Unity ML Toolkit SDK Gain insights into practical neural networks for training Agent Brain in the context of Unity ML Agents Create different models and perform hyper-parameter tuning Understand the Brain-Academy architecture in Unity ML Agents Understand the Python-C# API interface during real-time training of neural networks Grasp the fundamentals of generic neural networks and their variants using Tensorflow Create simulations and visualize agents playing games in Unity Who This Book Is For Readers with preliminary programming and game development experience in Unity, and those with experience in Python and a general idea of machine learning
Hands On Reinforcement Learning For Games
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Author : Micheal Lanham
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-01-03
Hands On Reinforcement Learning For Games written by Micheal Lanham and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-03 with Computers categories.
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.
Game Audio Development With Unity 5 X
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Author : Micheal Lanham
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-06-30
Game Audio Development With Unity 5 X written by Micheal Lanham and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-30 with Computers categories.
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.
A Concise Introduction To Multiagent Systems And Distributed Artificial Intelligence
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Author : Nikos Vlassis
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2007
A Concise Introduction To Multiagent Systems And Distributed Artificial Intelligence written by Nikos Vlassis and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
Foundations Of Deep Reinforcement Learning
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Author : Laura Graesser
language : en
Publisher: Addison-Wesley Professional
Release Date : 2019-11-20
Foundations Of Deep Reinforcement Learning written by Laura Graesser and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Computers categories.
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.
An Algorithmic Perspective On Imitation Learning
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Author : Takayuki Osa
language : en
Publisher:
Release Date : 2018-03-27
An Algorithmic Perspective On Imitation Learning written by Takayuki Osa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-27 with Technology & Engineering categories.
Familiarizes machine learning experts with imitation learning, statistical supervised learning theory, and reinforcement learning. It also roboticists and experts in applied artificial intelligence with a broader appreciation for the frameworks and tools available for imitation learning.
Artificial Intelligence And Games
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Author : Georgios N. Yannakakis
language : en
Publisher: Springer
Release Date : 2018-02-17
Artificial Intelligence And Games written by Georgios N. Yannakakis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-17 with Computers categories.
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.
Deep Learning Illustrated
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Author : Jon Krohn
language : en
Publisher: Addison-Wesley Professional
Release Date : 2019-08-05
Deep Learning Illustrated written by Jon Krohn and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-05 with Computers categories.
"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.