Hands-On Reinforcement Learning for Games (eBook)

Implementing self-learning agents in games using artificial intelligence techniques

(Autor)

eBook Download: EPUB
2020
432 Seiten
Packt Publishing (Verlag)
978-1-83921-677-0 (ISBN)

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Hands-On Reinforcement Learning for Games - Micheal Lanham
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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 Features



  • Get to grips with the different reinforcement and DRL algorithms for game development


  • Learn how to implement components such as artificial agents, map and level generation, and audio generation


  • Gain insights into cutting-edge RL research and understand how it is similar to artificial general research



Book 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 learn



  • Understand how deep learning can be integrated into an RL agent


  • Explore basic to advanced algorithms commonly used in game development


  • Build agents that can learn and solve problems in all types of environments


  • Train a Deep Q-Network (DQN) agent to solve the CartPole balancing problem


  • Develop game AI agents by understanding the mechanism behind complex AI


  • Integrate all the concepts learned into new projects or gaming agents



Who 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.


Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlowKey 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 DescriptionWith 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 forIf 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.
Erscheint lt. Verlag 3.1.2020
Sprache englisch
Themenwelt Mathematik / Informatik Informatik Netzwerke
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte actor-critic method • algorithms • Artificial Intelligence • Deep Reinforcement Learning • DQN • game developer • Games • gaming agent • Markov decision process • OpenAI Gym • PyTorch • Reinforcement Learning • self-learning AI agent • tensorflow
ISBN-10 1-83921-677-8 / 1839216778
ISBN-13 978-1-83921-677-0 / 9781839216770
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Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belle­tristik und Sach­büchern. Der Fließ­text wird dynamisch an die Display- und Schrift­größe ange­passt. Auch für mobile Lese­geräte ist EPUB daher gut geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür die kostenlose Software Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür eine kostenlose App.
Geräteliste und zusätzliche Hinweise

Buying eBooks from abroad
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