Neural Networks In Unity
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Neural Networks In Unity
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Author : Abhishek Nandy
language : en
Publisher: Apress
Release Date : 2018-07-14
Neural Networks In Unity written by Abhishek Nandy and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-14 with Computers categories.
Learn the core concepts of neural networks and discover the different types of neural network, using Unity as your platform. In this book you will start by exploring back propagation and unsupervised neural networks with Unity and C#. You’ll then move onto activation functions, such as sigmoid functions, step functions, and so on. The author also explains all the variations of neural networks such as feed forward, recurrent, and radial. Once you’ve gained the basics, you’ll start programming Unity with C#. In this section the author discusses constructing neural networks for unsupervised learning, representing a neural network in terms of data structures in C#, and replicating a neural network in Unity as a simulation. Finally, you’ll define back propagation with Unity C#, before compiling your project. What You'll Learn Discover the concepts behind neural networks Work with Unity and C# See the difference between fully connected and convolutional neural networks Master neural network processing for Windows 10 UWP Who This Book Is For Gaming professionals, machine learning and deep learning enthusiasts.
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.
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.
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 Deep Learning For Games
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Author : Micheal Lanham
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-30
Hands On Deep 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 2019-03-30 with Computers categories.
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 2017 Game Ai Programming Third Edition
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Author : Raymundo Barrera
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-11
Unity 2017 Game Ai Programming Third Edition written by Raymundo Barrera 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 2018-01-11 with Computers categories.
Use Unity 2017 to create fun and unbelievable AI entities in your games with A*, Fuzzy logic and NavMesh Key Features Explore the brand-new Unity 2017 features that makes implementing Artificial Intelligence in your game easier than ever Use fuzzy logic concepts in your AI decision-making to make your characters more engaging Build exciting and richer games by mastering advanced Artificial Intelligence concepts such as Neural Networks Book Description Unity 2017 provides game and app developers with a variety of tools to implement Artificial Intelligence. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating your game's worlds and characters. This third edition with Unity will help you break down Artificial Intelligence into simple concepts to give you a fundamental understanding of the topic to build upon. 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 5. Further on you will learn to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM). Next you'll learn how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. You will then learn how to implement simple flocks and crowd's dynamics, key AI concepts. Moving on, you will learn how to implement a behavior tree through a game-focused example. Lastly, you'll combine fuzzy logic concepts with state machines and apply all the concepts in the book to build a simple tank game. What you will learn Understand the basic terminology and concepts in game AI Explore advanced AI Concepts such as Neural Networks Implement a basic finite state machine using state machine behaviors in Unity 2017 Create sensory systems for your AI and couple it with a Finite State Machine Wok with Unity 2017's built-in NavMesh features in your game Build believable and highly-efficient artificial flocks and crowds Create a basic behavior tree to drive a character's actions Who this book is for This book is intended for Unity developers with a basic understanding of C# and the Unity editor. Whether you're looking to build your first game or are looking to expand your knowledge as a game programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation.
Unity 2017 Game Ai Programming Third Edition
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Author : Ray Barrera
language : en
Publisher:
Release Date : 2018
Unity 2017 Game Ai Programming Third Edition written by Ray Barrera and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Internet games categories.
Use Unity 2017 to create fun and unbelievable AI entities in your games with A*, Fuzzy logic and NavMesh About This Book Explore the brand-new Unity 2017 features that makes implementing Artificial Intelligence in your game easier than ever Use fuzzy logic concepts in your AI decision-making to make your characters more engaging Build exciting and richer games by mastering advanced Artificial Intelligence concepts such as Neural Networks Who This Book Is For This book is intended for Unity developers with a basic understanding of C# and the Unity editor. Whether you're looking to build your first game or are looking to expand your knowledge as a game programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation. What You Will Learn Understand the basic terminology and concepts in game AI Explore advanced AI Concepts such as Neural Networks Implement a basic finite state machine using state machine behaviors in Unity 2017 Create sensory systems for your AI and couple it with a Finite State Machine Wok with Unity 2017's built-in NavMesh features in your game Build believable and highly-efficient artificial flocks and crowds Create a basic behavior tree to drive a character's actions In Detail Unity 2017 provides game and app developers with a variety of tools to implement Artificial Intelligence. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating your game's worlds and characters. This third edition with Unity will help you break down Artificial Intelligence into simple concepts to give you a fundamental understanding of the topic to build upon. 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 5. Further on you will learn to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM). Next you'll learn how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. You will then learn how to implement simple flocks and crowd's dynamics, key AI concepts. Moving on, you will learn how to implement a behavior tree through a game-focused example. Lastly, you'll combine fuzzy logic concepts with state machines and apply all ...
Unity 2017 Game Ai Programming Third Edition
DOWNLOAD
Author : Ray Barrera
language : en
Publisher:
Release Date : 2018-01-11
Unity 2017 Game Ai Programming Third Edition written by Ray Barrera and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-11 with Computers categories.
Use Unity 2017 to create fun and unbelievable AI entities in your games with A*, Fuzzy logic and NavMesh Key Features Explore the brand-new Unity 2017 features that makes implementing Artificial Intelligence in your game easier than ever Use fuzzy logic concepts in your AI decision-making to make your characters more engaging Build exciting and richer games by mastering advanced Artificial Intelligence concepts such as Neural Networks Book Description Unity 2017 provides game and app developers with a variety of tools to implement Artificial Intelligence. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating your game's worlds and characters. This third edition with Unity will help you break down Artificial Intelligence into simple concepts to give you a fundamental understanding of the topic to build upon. 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 5. Further on you will learn to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM). Next you'll learn how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. You will then learn how to implement simple flocks and crowd's dynamics, key AI concepts. Moving on, you will learn how to implement a behavior tree through a game-focused example. Lastly, you'll combine fuzzy logic concepts with state machines and apply all the concepts in the book to build a simple tank game. What you will learn Understand the basic terminology and concepts in game AI Explore advanced AI Concepts such as Neural Networks Implement a basic finite state machine using state machine behaviors in Unity 2017 Create sensory systems for your AI and couple it with a Finite State Machine Wok with Unity 2017's built-in NavMesh features in your game Build believable and highly-efficient artificial flocks and crowds Create a basic behavior tree to drive a character's actions Who this book is for This book is intended for Unity developers with a basic understanding of C# and the Unity editor. Whether you're looking to build your first game or are looking to expand your knowledge as a game programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation.
The Perceptions Of Symmetry Grace Unity Rhythm And Balance By Feed Forward Back Propagating Neural Networks
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Author : Franco Zambon
language : en
Publisher:
Release Date : 2001
The Perceptions Of Symmetry Grace Unity Rhythm And Balance By Feed Forward Back Propagating Neural Networks written by Franco Zambon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Artificial intelligence categories.
Complex Valued Neural Networks With Multi Valued Neurons
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Author : Igor Aizenberg
language : en
Publisher: Springer
Release Date : 2011-06-24
Complex Valued Neural Networks With Multi Valued Neurons written by Igor Aizenberg and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-24 with Technology & Engineering categories.
Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.