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Author: Micheal Lanham Publisher: Packt Publishing Ltd ISBN: 1789131863 Category : Computers Languages : en Pages : 197
Book Description
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 ISBN: 1789131863 Category : Computers Languages : en Pages : 197
Book Description
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: Dr. Davide Aversa Publisher: Packt Publishing Ltd ISBN: 1789531454 Category : Computers Languages : en Pages : 238
Book Description
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.
Author: Erik van der Spek Publisher: Springer Nature ISBN: 3030346447 Category : Computers Languages : en Pages : 491
Book Description
This book constitutes the refereed proceedings of the First IFIP TC 14 Joint International Conference on Entertainment Computing and Serious Games, ICEC-JCSG 2019, held in Arequipa, Peru, in November 2019. The 26 full papers, 5 short papers, and 16 poster, demonstration, and workshop papers presented were carefully reviewed and selected from 88 submissions. They cover a large range of topics at the multidisciplinary intersection of design, art, entertainment, interaction, computing, psychology, and numerous serious application domains. The papers are organized in the following topical sections: mixed reality; virtual reality; entertainment algorithms; game design and development; interaction technologies; measurement and effects; and serious game applications.
Author: Laura Graesser Publisher: Addison-Wesley Professional ISBN: 0135172489 Category : Computers Languages : en Pages : 629
Book Description
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: Jannicke Baalsrud Hauge Publisher: Springer Nature ISBN: 3030893944 Category : Computers Languages : en Pages : 549
Book Description
This book constitutes the refereed proceedings of the 20th IFIP TC 14 International Conference on Entertainment Computing, ICEC 2021, which was supposed to take place in Coimbra, Portugal, in November 2021. The 26 full papers, 13 short papers and 11 other papers presented were carefully reviewed and selected from 84 submissions. ICEC brings together researchers and practitioners from diverse backgrounds to discuss the multidisciplinary intersection of design, art, entertainment, interaction, computing, psychology in the fields of gaming and entertainment computing.
Author: Kohei Arai Publisher: Springer Nature ISBN: 3031184610 Category : Technology & Engineering Languages : en Pages : 948
Book Description
The seventh Future Technologies Conference 2022 was organized in a hybrid mode. It received a total of 511 submissions from learned scholars, academicians, engineers, scientists and students across many countries. The papers included the wide arena of studies like Computing, Artificial Intelligence, Machine Vision, Ambient Intelligence and Security and their jaw- breaking application to the real world. After a double-blind peer review process 177 submissions have been selected to be included in these proceedings. One of the prominent contributions of this conference is the confluence of distinguished researchers who not only enthralled us by their priceless studies but also paved way for future area of research. The papers provide amicable solutions to many vexing problems across diverse fields. They also are a window to the future world which is completely governed by technology and its multiple applications. We hope that the readers find this volume interesting and inspiring and render their enthusiastic support towards it.
Author: Ben Goertzel Publisher: Springer Nature ISBN: 3030937585 Category : Computers Languages : en Pages : 379
Book Description
This book constitutes the refereed proceedings of the 14th International Conference on Artificial General Intelligence, AGI 2021, held as a hybrid event in San Francisco, CA, USA, in October 2021. The 36 full papers presented in this book were carefully reviewed and selected from 50 submissions. The papers cover topics from foundations of AGI, to AGI approaches and AGI ethics, to the roles of systems biology, goal generation, and learning systems, and so much more.
Author: Micheal Lanham Publisher: Packt Publishing Ltd ISBN: 1788998766 Category : Computers Languages : en Pages : 379
Book Description
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: Micheal Lanham Publisher: Packt Publishing Ltd ISBN: 1839216778 Category : Computers Languages : en Pages : 420
Book Description
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: Georgios N. Yannakakis Publisher: Springer ISBN: 3319635190 Category : Computers Languages : en Pages : 350
Book Description
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
Author: Micheal Lanham
Author: Dr. Davide Aversa
Author: Erik van der Spek
Author: Laura Graesser
Author: Jannicke Baalsrud Hauge
Author: Kohei Arai
Author: Ben Goertzel
Author: Micheal Lanham
Author: Micheal Lanham
Author: Georgios N. Yannakakis